{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "escsGkWFtzVj"
      },
      "source": [
        "# **Ensemble Stacking Algorithm using Combination of Machine Learning Algorithms for Diabetes Prediction and Classification Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A69jIUa4uq3Y"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.cm as cm\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b802sghBgmZi"
      },
      "outputs": [],
      "source": [
        "#import the data\n",
        "df = pd.read_csv(\"//content//diabetes.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iWSQaMxJgmZi",
        "outputId": "bb63010d-2eb8-4a7c-b5cd-5262031c4184",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-f4020869-1d4a-4a17-86a9-3feb668d9cda\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4020869-1d4a-4a17-86a9-3feb668d9cda')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-20a51526-9c37-4aab-a2bf-49b8c02dadcb\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20a51526-9c37-4aab-a2bf-49b8c02dadcb')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-20a51526-9c37-4aab-a2bf-49b8c02dadcb button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4020869-1d4a-4a17-86a9-3feb668d9cda button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f4020869-1d4a-4a17-86a9-3feb668d9cda');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 570
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ku6LAKIZgmZk",
        "outputId": "3c041573-e44e-48aa-a57a-4f03f84ee190",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZBvnK4WHgmZk",
        "outputId": "037870d9-6858-466d-fe2e-88aed5c9a7da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 572
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,:-1].values\n",
        "x\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7ZxzGYT_LSB",
        "outputId": "5fedc6d4-92d4-4b78-d704-d2361c7b0e6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  6.   , 148.   ,  72.   , ...,  33.6  ,   0.627,  50.   ],\n",
              "       [  1.   ,  85.   ,  66.   , ...,  26.6  ,   0.351,  31.   ],\n",
              "       [  8.   , 183.   ,  64.   , ...,  23.3  ,   0.672,  32.   ],\n",
              "       ...,\n",
              "       [  5.   , 121.   ,  72.   , ...,  26.2  ,   0.245,  30.   ],\n",
              "       [  1.   , 126.   ,  60.   , ...,  30.1  ,   0.349,  47.   ],\n",
              "       [  1.   ,  93.   ,  70.   , ...,  30.4  ,   0.315,  23.   ]])"
            ]
          },
          "metadata": {},
          "execution_count": 573
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,-1].values\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EZw1taK_2GW",
        "outputId": "7ec4324f-1fd8-4b41-9832-dba5588aa8ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
              "       1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
              "       1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
              "       0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,\n",
              "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 574
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import RandomOverSampler,SMOTE\n",
        "ro=RandomOverSampler()\n",
        "x_data,y_data=ro.fit_resample(x,y)"
      ],
      "metadata": {
        "id": "TBzGhb35_68K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from imblearn.under_sampling import NearMiss\n",
        "# from imblearn.over_sampling import SMOTE\n",
        "# nm = NearMiss()\n",
        "# smote = SMOTE()"
      ],
      "metadata": {
        "id": "_fRk36T1GT9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "print(\"Actual Data:\",Counter(y))\n",
        "print(\"Artificial Data:\",Counter(y_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vE3BafPEAA7I",
        "outputId": "eb2450a2-3a33-44ec-b07a-5324df94e151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual Data: Counter({0: 500, 1: 268})\n",
            "Artificial Data: Counter({1: 500, 0: 500})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wXI01LUtgmZl",
        "outputId": "25a201f6-90ff-46ee-8006-f42b1453e729",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-578-1819b63546cb>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df[\"Age\"],kde=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age'>"
            ]
          },
          "metadata": {},
          "execution_count": 578
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.distplot(df[\"Age\"],kde=False)\n",
        "#most of the female patients are from the age group of 20-30"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a1ann-1ogmZl",
        "outputId": "996b4b44-a9d0-43ce-ff9b-74f1e25c4ce3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7f0df920ae30>,\n",
              "  <matplotlib.patches.Wedge at 0x7f0df920ad40>],\n",
              " [Text(-0.9801072140121813, -0.49938947630209485, 'Non - Diabetic'),\n",
              "  Text(1.0246575908033213, 0.5220889020168246, 'Diabetic')],\n",
              " [Text(-0.5346039349157352, -0.2723942598011426, '65%'),\n",
              "  Text(0.5791542904540511, 0.2950937272269008, '35%')])"
            ]
          },
          "metadata": {},
          "execution_count": 579
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "labels = ('Non - Diabetic','Diabetic')\n",
        "fracs = [65,35]\n",
        "total = sum(fracs)\n",
        "explode = (0, 0.05)\n",
        "plt.pie(fracs, explode=explode, labels=labels, autopct=lambda p: '{:.0f}%'.format(p * total / 100), shadow=True, startangle=90)\n",
        "#plt.title(\"Distribution of Diabetic and Non-diabetic patients\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ye4bNiwSgmZm",
        "outputId": "efd1ce34-982e-4603-9c23-0f26830a6e39",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 580
        }
      ],
      "source": [
        "#Checking if any missing values are present in the dataset\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BjLhvs3ZgmZn",
        "outputId": "22dc8cbe-b5e0-4d6f-8a81-644c1d83265d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 581
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Checking correlation between the input features of the data\n",
        "sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LWQcT27WgmZn",
        "outputId": "a32ca63f-b757-49cc-a59e-26ca83d699f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='BMI', ylabel='SkinThickness'>"
            ]
          },
          "metadata": {},
          "execution_count": 582
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#BMI and SkinThickness have some correlation\n",
        "sns.scatterplot(x=df[\"BMI\"],y=df[\"SkinThickness\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "roxPlm8ShaJs"
      },
      "outputs": [],
      "source": [
        "# X=df.drop(['Outcome'], axis=1)\n",
        "# print(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cyAOBjc5hwf_"
      },
      "outputs": [],
      "source": [
        "# y=df['Outcome']\n",
        "# print(y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sd=StandardScaler()\n",
        "x=sd.fit_transform(x)\n"
      ],
      "metadata": {
        "id": "mA1KgiTX81sJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "x= scaler.fit_transform(x)"
      ],
      "metadata": {
        "id": "pE3LKS1DCG1I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k9vHDORkwJE5"
      },
      "outputs": [],
      "source": [
        "# Remove low variance features\n",
        "# from sklearn.feature_selection import VarianceThreshold\n",
        "\n",
        "# selection = VarianceThreshold(threshold=(0.1))\n",
        "# X = selection.fit_transform(X)\n",
        "# X.shape\n",
        "# print(X)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rrqWqKeoAZ11"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abJXshUhgmZp"
      },
      "source": [
        "# **Data Splitting**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lpns5FPdaQgq"
      },
      "outputs": [],
      "source": [
        "# from sklearn.model_selection import train_test_split\n",
        "\n",
        "# X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=100)\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(x_data,y_data,test_size=0.2,random_state=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGQmqYf2ObLw",
        "outputId": "92f90b72-2155-48c4-b1b7-ca10124c022b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((800, 8), (200, 8))"
            ]
          },
          "metadata": {},
          "execution_count": 589
        }
      ],
      "source": [
        "X_train.shape, X_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0UpZfDRyvb5t"
      },
      "source": [
        "# **Building Classification models**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q8yCRtbQu5-F"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import matthews_corrcoef\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import recall_score\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LswMOe9Y26Nm"
      },
      "source": [
        "**K nearest neighbors**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fI6Ni6i3EAy",
        "outputId": "8b548a38-e7ad-48cc-bcf3-6c644a1ddafe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model performance for Training set\n",
            "- Accuracy: 86.5\n",
            "- MCC: 73.62599090397252\n",
            "- F1 score: 86.46195420726971\n",
            "- Recall: 92.6208651399491\n",
            "----------------------------------\n",
            "Model performance for Test set\n",
            "- Accuracy: 72.0\n",
            "- MCC: 43.568568372017246\n",
            "- F1 score: 71.7778455904791\n",
            "- Recall: 79.43925233644859\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "accuracy_scores = np.zeros(5)\n",
        "accuracy_scores_train = np.zeros(5)\n",
        "knn = KNeighborsClassifier(3) # Define classifier\n",
        "knn.fit(X_train, y_train) # Train model\n",
        "\n",
        "# Make predictions\n",
        "y_train_pred = knn.predict(X_train)\n",
        "y_test_pred = knn.predict(X_test)\n",
        "\n",
        "# Training set performance\n",
        "knn_train_accuracy = accuracy_score(y_train, y_train_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores_train[0] = knn_train_accuracy\n",
        "knn_train_mcc = matthews_corrcoef(y_train, y_train_pred)*100 # Calculate MCC\n",
        "knn_train_f1 = f1_score(y_train, y_train_pred, average='weighted')*100 # Calculate F1-score\n",
        "knn_train_recall = recall_score(y_train, y_train_pred)*100# Calculate recall\n",
        "\n",
        "# Test set performance\n",
        "knn_test_accuracy = accuracy_score(y_test, y_test_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores[0] = knn_test_accuracy\n",
        "knn_test_mcc = matthews_corrcoef(y_test, y_test_pred)*100 # Calculate MCC\n",
        "knn_test_f1 = f1_score(y_test, y_test_pred, average='weighted')*100 # Calculate F1-score\n",
        "knn_test_recall = recall_score(y_test, y_test_pred)*100# Calculate recall\n",
        "\n",
        "print('Model performance for Training set')\n",
        "print('- Accuracy: %s' % knn_train_accuracy)\n",
        "print('- MCC: %s' % knn_train_mcc)\n",
        "print('- F1 score: %s' % knn_train_f1)\n",
        "print('- Recall: %s' % knn_train_recall)\n",
        "print('----------------------------------')\n",
        "print('Model performance for Test set')\n",
        "print('- Accuracy: %s' % knn_test_accuracy)\n",
        "print('- MCC: %s' % knn_test_mcc)\n",
        "print('- F1 score: %s' % knn_test_f1)\n",
        "print('- Recall: %s' % knn_test_recall)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojasbTOn4-x-"
      },
      "source": [
        "**Support vector machine (Radial basis function kernel)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ot6hHeU04-2j",
        "outputId": "ee12fbd4-7c6c-45c0-d659-19a8d71be8e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model performance for Training set\n",
            "- Accuracy: 100.0\n",
            "- MCC: 100.0\n",
            "- F1 score: 100.0\n",
            "- Recall: 100.0\n",
            "----------------------------------\n",
            "Model performance for Test set\n",
            "- Accuracy: 84.0\n",
            "- MCC: 72.21463335798389\n",
            "- F1 score: 83.7677185200121\n",
            "- Recall: 70.09345794392523\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svm_rbf = SVC(gamma=2, C=1)\n",
        "svm_rbf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_train_pred = svm_rbf.predict(X_train)\n",
        "y_test_pred = svm_rbf.predict(X_test)\n",
        "\n",
        "# Training set performance\n",
        "svm_rbf_train_accuracy = accuracy_score(y_train, y_train_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores_train[1] = svm_rbf_train_accuracy\n",
        "svm_rbf_train_mcc = matthews_corrcoef(y_train, y_train_pred)*100 # Calculate MCC\n",
        "svm_rbf_train_f1 = f1_score(y_train, y_train_pred, average='weighted')*100 # Calculate F1-score\n",
        "svm_rbf_train_recall = recall_score(y_train, y_train_pred)*100# Calculate recall\n",
        "\n",
        "# Test set performance\n",
        "svm_rbf_test_accuracy = accuracy_score(y_test, y_test_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores[1] = svm_rbf_test_accuracy\n",
        "svm_rbf_test_mcc = matthews_corrcoef(y_test, y_test_pred)*100 # Calculate MCC\n",
        "svm_rbf_test_f1 = f1_score(y_test, y_test_pred, average='weighted')*100 # Calculate F1-score\n",
        "svm_rbf_test_recall = recall_score(y_test, y_test_pred)*100# Calculate recall\n",
        "\n",
        "print('Model performance for Training set')\n",
        "print('- Accuracy: %s' % svm_rbf_train_accuracy)\n",
        "print('- MCC: %s' % svm_rbf_train_mcc)\n",
        "print('- F1 score: %s' % svm_rbf_train_f1)\n",
        "print('- Recall: %s' % svm_rbf_train_recall)\n",
        "print('----------------------------------')\n",
        "print('Model performance for Test set')\n",
        "print('- Accuracy: %s' % svm_rbf_test_accuracy)\n",
        "print('- MCC: %s' % svm_rbf_test_mcc)\n",
        "print('- F1 score: %s' % svm_rbf_test_f1)\n",
        "print('- Recall: %s' % svm_rbf_test_recall)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tus32H-i42PT"
      },
      "source": [
        "**Decision tree**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3YJF0rz44Ar",
        "outputId": "69d30468-6ec8-4519-f498-96920dbb8aee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model performance for Training set\n",
            "- Accuracy: 83.375\n",
            "- MCC: 67.04720958757348\n",
            "- F1 score: 83.35589406137666\n",
            "- Recall: 87.53180661577609\n",
            "----------------------------------\n",
            "Model performance for Test set\n",
            "- Accuracy: 73.0\n",
            "- MCC: 45.58370315403485\n",
            "- F1 score: 72.83563311688312\n",
            "- Recall: 79.43925233644859\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(max_depth=5) # Define classifier\n",
        "dt.fit(X_train, y_train) # Train model\n",
        "\n",
        "# Make predictions\n",
        "y_train_pred = dt.predict(X_train)\n",
        "y_test_pred = dt.predict(X_test)\n",
        "\n",
        "# Training set performance\n",
        "dt_train_accuracy = accuracy_score(y_train, y_train_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores_train[2] = dt_train_accuracy\n",
        "dt_train_mcc = matthews_corrcoef(y_train, y_train_pred)*100 # Calculate MCC\n",
        "dt_train_f1 = f1_score(y_train, y_train_pred, average='weighted')*100 # Calculate F1-score\n",
        "dt_train_recall = recall_score(y_train, y_train_pred)*100# Calculate recall\n",
        "\n",
        "# Test set performance\n",
        "dt_test_accuracy = accuracy_score(y_test, y_test_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores[2] = dt_test_accuracy\n",
        "dt_test_mcc = matthews_corrcoef(y_test, y_test_pred)*100 # Calculate MCC\n",
        "dt_test_f1 = f1_score(y_test, y_test_pred, average='weighted')*100 # Calculate F1-score\n",
        "dt_test_recall = recall_score(y_test, y_test_pred)*100# Calculate recall\n",
        "\n",
        "print('Model performance for Training set')\n",
        "print('- Accuracy: %s' % dt_train_accuracy)\n",
        "print('- MCC: %s' % dt_train_mcc)\n",
        "print('- F1 score: %s' % dt_train_f1)\n",
        "print('- Recall: %s' % dt_train_recall)\n",
        "print('----------------------------------')\n",
        "print('Model performance for Test set')\n",
        "print('- Accuracy: %s' % dt_test_accuracy)\n",
        "print('- MCC: %s' % dt_test_mcc)\n",
        "print('- F1 score: %s' % dt_test_f1)\n",
        "print('- Recall: %s' % dt_test_recall)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXd2iTxuviDb"
      },
      "source": [
        "**Random forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4iahxJWvhVu",
        "outputId": "1375f214-4f4b-47fc-b2e3-09b80c4f54d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model performance for Training set\n",
            "- Accuracy: 98.625\n",
            "- MCC: 97.25616191106367\n",
            "- F1 score: 98.62479578324648\n",
            "- Recall: 97.96437659033079\n",
            "----------------------------------\n",
            "Model performance for Test set\n",
            "- Accuracy: 82.5\n",
            "- MCC: 64.78592214066782\n",
            "- F1 score: 82.45805898231539\n",
            "- Recall: 85.98130841121495\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators=10) # Define classifier\n",
        "rf.fit(X_train, y_train) # Train model\n",
        "\n",
        "# Make predictions\n",
        "y_train_pred = rf.predict(X_train)\n",
        "y_test_pred = rf.predict(X_test)\n",
        "\n",
        "# Training set performance\n",
        "rf_train_accuracy = accuracy_score(y_train, y_train_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores_train[3] =   rf_train_accuracy\n",
        "rf_train_mcc = matthews_corrcoef(y_train, y_train_pred)*100 # Calculate MCC\n",
        "rf_train_f1 = f1_score(y_train, y_train_pred, average='weighted')*100 # Calculate F1-score\n",
        "rf_train_recall = recall_score(y_train, y_train_pred)*100# Calculate recall\n",
        "\n",
        "# Test set performance\n",
        "rf_test_accuracy = accuracy_score(y_test, y_test_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores[3] =   rf_test_accuracy\n",
        "rf_test_mcc = matthews_corrcoef(y_test, y_test_pred)*100 # Calculate MCC\n",
        "rf_test_f1 = f1_score(y_test, y_test_pred, average='weighted')*100 # Calculate F1-score\n",
        "rf_test_recall = recall_score(y_test, y_test_pred)*100# Calculate recall\n",
        "\n",
        "print('Model performance for Training set')\n",
        "print('- Accuracy: %s' % rf_train_accuracy)\n",
        "print('- MCC: %s' % rf_train_mcc)\n",
        "print('- F1 score: %s' % rf_train_f1)\n",
        "print('- Recall: %s' % rf_train_recall)\n",
        "print('----------------------------------')\n",
        "print('Model performance for Test set')\n",
        "print('- Accuracy: %s' % rf_test_accuracy)\n",
        "print('- MCC: %s' % rf_test_mcc)\n",
        "print('- F1 score: %s' % rf_test_f1)\n",
        "print('- Recall: %s' % rf_test_recall)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fla3COGbgmZs"
      },
      "source": [
        "**Stacking Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_hpkaoBygmZs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b4df0fa-dfef-42c4-9ce9-7287e9e72de7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model performance for Training set\n",
            "- Accuracy: 100.0\n",
            "- MCC: 100.0\n",
            "- F1 score: 100.0\n",
            "- Recall: 100.0\n",
            "----------------------------------\n",
            "Model performance for Test set\n",
            "- Accuracy: 87.5\n",
            "- MCC: 75.52556121842\n",
            "- F1 score: 87.5103148208347\n",
            "- Recall: 83.17757009345794\n"
          ]
        }
      ],
      "source": [
        "# Define estimators\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "forest = RandomForestClassifier(n_estimators=10)\n",
        "knn = knn = KNeighborsClassifier(3)\n",
        "svm_rbf = SVC(gamma=2, C=1)\n",
        "dt = DecisionTreeClassifier(max_depth=5)\n",
        "\n",
        "lgclassifier = LogisticRegression(penalty='l2', random_state=100)\n",
        "\n",
        "estimator_list = [\n",
        "    ('dt',dt),\n",
        "    ('rf',forest),\n",
        "    ('knn',knn),\n",
        "    ('svm_rbf',svm_rbf)]\n",
        "\n",
        "# Build stack model\n",
        "stack_model = StackingClassifier(\n",
        "    estimators=estimator_list,\n",
        "    final_estimator=lgclassifier,\n",
        "    cv=20\n",
        ")\n",
        "\n",
        "# Train stacked model\n",
        "stack_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_train_pred = stack_model.predict(X_train)\n",
        "y_test_pred = stack_model.predict(X_test)\n",
        "\n",
        "# Training set model performance\n",
        "stack_model_train_accuracy = accuracy_score(y_train, y_train_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores_train[4] = stack_model_train_accuracy\n",
        "stack_model_train_mcc = matthews_corrcoef(y_train, y_train_pred)*100 # Calculate MCC\n",
        "stack_model_train_f1 = f1_score(y_train, y_train_pred, average='weighted')*100 # Calculate F1-score\n",
        "stack_model_train_recall = recall_score(y_train, y_train_pred)*100# Calculate recall\n",
        "\n",
        "\n",
        "# Test set model performance\n",
        "stack_model_test_accuracy = accuracy_score(y_test, y_test_pred)*100 # Calculate Accuracy\n",
        "accuracy_scores[4] = stack_model_test_accuracy\n",
        "stack_model_test_mcc = matthews_corrcoef(y_test, y_test_pred)*100 # Calculate MCC\n",
        "stack_model_test_f1 = f1_score(y_test, y_test_pred, average='weighted')*100 # Calculate F1-score\n",
        "stack_model_test_recall = recall_score(y_test, y_test_pred)*100# Calculate recall\n",
        "\n",
        "print('Model performance for Training set')\n",
        "print('- Accuracy: %s' % stack_model_train_accuracy)\n",
        "print('- MCC: %s' % stack_model_train_mcc)\n",
        "print('- F1 score: %s' % stack_model_train_f1)\n",
        "print('- Recall: %s' % stack_model_train_recall)\n",
        "print('----------------------------------')\n",
        "print('Model performance for Test set')\n",
        "print('- Accuracy: %s' % stack_model_test_accuracy)\n",
        "print('- MCC: %s' % stack_model_test_mcc)\n",
        "print('- F1 score: %s' % stack_model_test_f1)\n",
        "print('- Recall: %s' % stack_model_test_recall)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_vCGuAyqgmZt",
        "outputId": "62bc29bf-72a2-4cec-8918-8b1e142ff3e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  0.,  20.,  40.,  60.,  80., 100., 120.]),\n",
              " [Text(0, 0.0, '0'),\n",
              "  Text(0, 20.0, '20'),\n",
              "  Text(0, 40.0, '40'),\n",
              "  Text(0, 60.0, '60'),\n",
              "  Text(0, 80.0, '80'),\n",
              "  Text(0, 100.0, '100'),\n",
              "  Text(0, 120.0, '120')])"
            ]
          },
          "metadata": {},
          "execution_count": 596
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "colors = cm.rainbow(np.linspace(0, 1, 4))\n",
        "labels = ['K nearest neighbors','Support vector machine','Decision Tree', 'Random Forest','Stacked Classifier']\n",
        "p1 = plt.bar(labels,\n",
        "        accuracy_scores_train,\n",
        "        color = colors)\n",
        "for i in p1:\n",
        "    height = i.get_height()\n",
        "    plt.annotate( \"{}%\".format(height),(i.get_x() + i.get_width()/2, height+.05),ha=\"center\",va=\"bottom\",fontsize=15)\n",
        "\n",
        "plt.xlabel('Classifiers',fontsize=18)\n",
        "plt.ylabel('Accuracy',fontsize=18)\n",
        "plt.title('Training accuracy of algorithms',fontsize=20)\n",
        "plt.xticks(rotation=45,fontsize=12)\n",
        "plt.yticks(fontsize=12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "XmR76RCvgmZt",
        "outputId": "f1731fb8-0f5c-4350-af31-7f8d8236d046",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 997
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  0.,  20.,  40.,  60.,  80., 100.]),\n",
              " [Text(0, 0.0, '0'),\n",
              "  Text(0, 20.0, '20'),\n",
              "  Text(0, 40.0, '40'),\n",
              "  Text(0, 60.0, '60'),\n",
              "  Text(0, 80.0, '80'),\n",
              "  Text(0, 100.0, '100')])"
            ]
          },
          "metadata": {},
          "execution_count": 597
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/IAAANbCAYAAADhcaQeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADpnklEQVR4nOzdd3xO9///8ecVsknMIEZQOyihqF2tVaP2rk3tqrZaSo1W+ZhFzZq1arRao9SesYJYDaFFjRi1EiEhyfn94ZfrK7IHcXjcb7fcKue8zzmv6+TKaZ7X+33ex2IYhiEAAAAAAGAKNqldAAAAAAAASDiCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAC8JvLmzSuLxaKOHTumdinASy88PFyTJ09WuXLl5OLiIovFIovFokaNGr3QOhYsWGA99oULF17osZ+njh07ymKxKG/evMnaz4ULF6znZ8GCBSlSGwCYQdrULgAAXgYXLlxQvnz5kr0fwzBSoBoAqa1169ZauXJlapcBAECM6JEHABOrXr26LBaLqlevntqlAK8Mb29va4ivV6+eNm/erOPHj+vEiROaMmVKKlf3emAEEQDEjR55AJCUM2dOnThxItb1JUqUkCSVLVtW8+fPf1FlpahXaVgu8Dxt2bJFkpQmTRotXbpULi4uqVzRq2fBggUMhQeAZCDIA4AkW1tbFS9ePN52zs7OCWoHwLyuXLkiScqWLRshHgDwUmJoPQAAwFNCQ0MlPfmADwCAlxFBHgBSyJEjR9SjRw8VLlxY6dKlk7OzswoXLqyePXvK398/zm3v3r2rUaNG6e2331bGjBlla2urrFmzqlixYmrcuLFmzJih69evW9tHzvi8c+dOSdLOnTutMzdHfj07G3Rc95zu2LHDut2OHTskSStWrNC7776rrFmzytHRUYULF9bAgQN1+/bteM/Fv//+q549eypfvnxycHCQu7u7GjVqpO3bt0uShg8fbj1eUt25c0fz589Xu3btVKxYMaVLl052dnbKnj27ateurdmzZ+vRo0cJ2tfNmzc1cuRIVapUSW5ubrK1tVXGjBlVvnx5DRw4UMePH49129DQUM2ePVv16tVTzpw5ZW9vL2dnZ3l6eqpr1676888/o02CmNC5DeI7T5Hrhg8fLknatm2bmjdvrty5c8vW1jbae+DkyZP69ttvVbt2beXKlUv29vZKly6dChYsqA4dOmj//v3xnqtIe/fuVdeuXVW4cGG5uLjIzs5OuXLlUv369TVt2jTdvXvX2rZJkyayWCzKmDGjQkJC4txvWFiYsmfPLovFovfffz/B9TzrxIkT6t69uwoWLCgnJyelT59enp6e+uSTT2K9zSTyfC5cuFCSdPHixWi/V4mVkuc8vuO0b99euXLlkoODg/LkyaN27drpyJEjkhI+S/yePXv04YcfKm/evHJwcFCGDBlUunRpDRkyRDdv3ox1u2evIREREZo3b57eeecdZcuWTTY2NlGuPbHVE/m7cfHiRUnSwoULo/0M4vu92bx5sxo0aKDs2bPL3t5e+fLlU8+ePXX58uVYt3n2dy0wMFDDhw9XiRIllC5dOrm5uen999+Xt7d3lO1u3LihIUOGyNPTU87OzsqcObM++OADHT16NM4aE3vNB4BoDABAvCQZkoxq1apFWxceHm588sknhsVisbZ79itt2rTGrFmzYtz3X3/9Zbi7u8e6beTX1KlTrdt06NAh3vYeHh5RjuPh4WFIMjp06BCthu3bt1u327p1q9GuXbtY91ugQAEjICAg1nO1detWI126dDFua7FYjFGjRhnDhg2zLkuqyNcT11fp0qXjrNUwDGPx4sWGs7Nzos5lpKNHjxr58uWLt47z589H2a5atWqxvp+eFt95ilw3bNgwY/DgwXHW/fTPOK6vL7/8Ms6aHjx4YLRu3Tre/QwbNsy6zfr1663Lly1bFuf+f//9d2vblStXxtk2Nt99951hY2MTa2329vbGwoULo22XkPOTGClxzufPnx/r+yjSokWLDFtb2xj3bWtrayxYsMB6zYjtvRweHm707t07zjpdXV2NTZs2xftaN2zYYLz33nvRtn/62hNbPZG/G3F9Pf17c/78eevy+fPnG19++WWs22XNmtX466+/Yqz/6d+1f//91yhUqFCM+0iTJo2xYsUKwzAM49ixY0bOnDljfY9t27YtxmMl5ZoPAM/iHnkASKa+fftq+vTpkqSqVauqY8eOyp8/v5ycnHTs2DF9//33OnXqlD766CNlz55dDRs2jLL9hx9+qKtXr8rW1lbdunVT3bp1lT17dkVEROjy5cvav3+/Vq9eHWWbUaNG6bPPPlOnTp3k4+MT4yR8dnZ2SXo9Q4cOlbe3txo1aqT27dvLw8ND169f17Rp07R+/XqdO3dOn3zyiZYtWxZt23/++UcNGzZUcHCw0qZNq549e6pRo0ZycXHRyZMnNW7cOH311VcqX758kmp7Wnh4uMqXL6/69eurdOnSypYtmx49eqTz589r8eLF2rhxo44ePapWrVpZRxk8a9GiRWrfvr0kycHBIcr5v3//vo4fP641a9bo7Nmz0bb18/NTlSpVdP/+fUlS48aN1apVK+XPn1/h4eHy9/fXpk2bov3snodff/1VJ06cUIkSJfTJJ5+oePHievjwoXx9fa1twsLC5OzsrHr16qlGjRoqUqSIXFxcdOPGDZ06dUpTpkzRxYsXNWbMGBUqVEidOnWKdpyIiAh98MEH2rx5sySpYMGC6tWrl8qWLSsnJycFBATI29tbK1asiLJdnTp1lCtXLl2+fFnz589Xq1atYn0tke/jLFmyRPtdSYjp06dr8ODBkqSsWbPqiy++UKVKlRQeHq4tW7Zo3LhxCg4OVseOHZUlS5Yovf6RE14OGTJEv//+u9zd3fXnn38muoZIKXHO4+Pt7a2OHTsqPDxcTk5OGjBggGrVqiV7e3v5+Pho9OjR6t69uzw9PePcz5dffqlp06ZJkvLly6cvvvhCXl5eCg4O1po1a/TDDz/o3r17ql+/vg4ePKg333wz1n198cUXOn78uBo2bKiOHTtaryGBgYHxvp758+crODhYtWvX1tWrV/XBBx/o22+/jdLG2dk5xm1//PFHeXt7q1q1avroo49UqFAh3b17Vz/99JN++ukn3bx5U507d9a+ffvirKF58+a6fPmyBg0apDp16sjJyUl79uzRsGHDFBgYqC5duqhs2bKqX7++Hj58qFGjRqlatWqytbXVxo0bNWrUKIWGhqpjx446e/ZstGtxUq75ABBNan+SAABmoBh6ggzDMDZt2mRdN2fOnBi3ffjwoVGjRg1r79Pjx4+t6/7+++8E9b5EREQYt2/fjrY8oT27hpHwHnlJxrfffhtjDbVq1TKkJyMMbty4Ea1No0aNrPtYvXp1tPXBwcFGuXLlktzD+TR/f/8418+bN896jC1btkRbf/XqVcPJycmQZLi5uRknTpyIdV///vtvtGVeXl6GJMPGxibOXub//vvPePDgQZRlKd0jL8l49913jZCQkFj3dfPmTePOnTuxrg8NDTVq1qxpfZ+GhYVFazN58mTr8Ro3bhzr8cLDw43Lly9HWTZ06FDr+bp06VKM212/ft3as/zxxx/HWmtsbty4Yf2Zuru7x/hzO3LkiHUERs6cOY1Hjx5FaxNf73VCpcQ5j69HvlSpUtYe4P3790dbf/36dSN//vzWfcT0mo4fP24dwVC8ePEYa96wYYO1Tbly5aKtf/YaMmTIkFhft2HEf47jul497ekeeUlGt27djIiIiGjtunbtam1z5MiRaOuf/l2L7VyuW7cuSu9+lixZjHPnzkVrN23aNGu7X3/9Ncq65F7zASAS98gDQDKMGTNGktS0aVN16dIlxjYODg764YcfJD255zbyPnFJunbtmvXfVatWjfU4kfcXvwhlypSx9mg+W8OAAQMkPelpfLZX6+rVq1q7dq0kqVmzZmrUqFG0fTg5OWn27NkpUmfBggXjXN+pUyeVKlVKkvTbb79FWz916lQ9ePBAkjR79uw4n0aQO3fuKN9v2rTJeu9xv3794uxhzpw5sxwdHeOsNblsbGw0Z84c2dvbx9omS5YsypAhQ6zr7ezsNG7cOElP3qdP9+ZLT3rjI9fnypVLP/30U6zHs7GxUc6cOaMs69y5sywWiyIiIqz3oD9r8eLFevz4sbV9Ys2fP9/6M504cWK0n5sklS5dWoMGDZL0ZHb6mN4bKSW55zw+Bw4csG7Tt2/fGEe6uLm5adKkSXHuZ8aMGYqIiJAkzZkzJ8aa69SpY/2ZHDx4UIcOHYp1f4UKFbLO2/Ai5ciRQ1OnTo1xLoPPPvvM+u/du3fHuZ/+/fvHeC7r1asnDw8PSU/m1fjmm2/0xhtvRGvXqVMnOTg4xHisl/GaD8CcCPIAkESBgYHWIdvNmjWLs23RokWVJUsWSYoSgHPkyGH998vyTOU2bdrEOqlXmTJlrP/+559/oqzbvn27wsPDJT0ZOhqbN998M85huUlhGIauXbsmf39/nTx50voVGSaPHTsWbZt169ZJkvLnz5/oIdyR20pP/uhPbZUqVYp3ErNnhYaG6t9//9Vff/1lPV/GU5PyPXvOfH19rZOFdevWTenSpUvU8fLmzav33ntPUuzv9chh9WXKlFHJkiUTtX/p/57/niFDBjVp0iTWdl27do22zYuQ2HMen6drj+t3rl69esqcOXO8+/H09Izztpdu3brFeOxntWzZUmnSpIl1/fPSrFmzWD9cipyEVIp+7XpWXB/MRb4vLRaLWrZsGWMbR0dH6weNzx7rZbzmAzAngjwAJNHRo0etvVitW7eONrPys1///fefpKg9Mvny5VOVKlUkSZMmTZKnp6e+/vprbdu2zdqz+KIVKVIk1nWZMmWy/jsoKCjKupMnT1r//XTgj0nZsmWTWF1U69evV/369eXq6qocOXKocOHCKlGihPVr/fr1kmQ995EeP35srbdy5cqJno08ckbqPHnyWHvoUlNCQ29wcLBGjx6tN998U87OzvLw8JCnp6f1fJUuXdra9tlz9vQs3JHv2cSKDNDnzp2L1lPp4+Nj/ZkkpTde+r/3oJeXV5yPjsuWLZv1g4+n37fPQ3LOeXwia7e3t4/zHvg0adJYR6c8KzQ01DoHRHxzV5QuXdp6XuM6b0n5ECYlxHXtkmTt4X722vWsQoUKxboucrRClixZ4uwxj2z37LFexms+AHMiyANAEt24cSNJ2z37x9qyZcv09ttvS5L++usvffPNN3r33XeVIUMGVa1aVTNnzoz3kV0pycnJKdZ1Njb/97+NyN73SHfu3LH+O2vWrHEeI7718TEMQ127dlX9+vW1fv36eP8wf/jwYZTvb9++be0JfbqHLKEiA1dStn0eEjIE98KFCypRooQGDx6s48ePR/v5PevZc/Z0yEzq627UqJF1ZMqzkzPOmzdP0pNbUdq0aZOk/Uc+GtHNzS3ettmzZ4+yzfOQ3HMen8jfuUyZMsXbAx7b79zTv7fxnTdbW1trz35c5y21hoTHde2S/u/6Fd/PISHXwOQc62W75gMwJ4I8ACTR03+gzZo1SydOnEjQ16hRo6LsJ2fOnPL29taWLVvUq1cveXp6ymKx6PHjx9q9e7d69uyp4sWLx/ss+tfJvHnzNHfuXElSqVKltGDBAvn5+SkwMFBhYWEyDEOGYViHGz89fPlVlJBhzB9++KHOnz8vi8Wizp07a9OmTbp06ZJCQkIUEREhwzCivKefxzmzs7Oz/kxWrlyp4OBgSVJISIj1KQiNGzeO877yhEjK896fh5fhnCdGSp231BhWbyZc8wGkBB4/BwBJ9PQ9p05OTnFOlpYQ7777rt59911J0q1bt7RlyxbNnj1b27Zt099//62WLVtGGd78snm6F+7mzZvRJjt72s2bN5N1rB9//FGSVKBAAXl7e8c6mVxsvYaZMmWSjY2NIiIiFBAQkOjjR/YqJ2Vb6f966yJvzYhNZNBNrtOnT2vPnj2SpMGDB0d7nFekuHpZI1+z9OR1xzeMOTZdu3bVpEmTdP/+fa1cuVIdO3bUb7/9prt370pK+rB66cnPNSAgQNevX4+3beQtLk/fLpKSUuKcxyfyd+727dsKDw+PM0DH9jv39O9tfOctLCxMt27dkvT8ztvrxOzXfACpix55AEiiUqVKWXuw9u7dm6L7zpw5s1q2bKmtW7daJ2Lz9fWN9jzzl6XnUVKUe3QPHz4cZ1sfH59kHevUqVOSpIYNG8Ya4g3DsM4s/yxbW1vrBy+7d+9OdE+ol5eXJOnff//VxYsXE7WtJKVPn15S1GHNMUmpHrnI8yUp1gm6pLh/LpGvWZJ27dqV5FqKFStmHVYcObw+cli9h4eHNdgkReTP9MiRIwoLC4u13Y0bN6w/t+R+ABeblDjn8Yn8nQsNDY1yvGeFh4fHOiO+vb29dWK2AwcOxHm8o0ePWp8q8LzOW6SX6dr2IiT0mg8AkQjyAJBEWbNmVYUKFSRJS5cuTXYvc2yeDjbPToYV+Yij0NDQ53LsxKhevbq1p3nRokWxtjt27FiiZ+d+VmRIi6vH+vfff4+zx7xBgwaSpPPnz+v3339P1PEjt5UU76O9YpIvXz5JT4J6bPf3//fff9q8eXOi9x2Tp0NtXOds5syZsa578803rY9zmzNnju7fv5/keiInvdu9e7e2b9+urVu3SpI6duyYrAAXOSv+3bt39euvv8babu7cudYPbyK3SWkpcc7j8/S1Ia7fufXr11t70mMSeQ5OnTqlgwcPxtpuzpw50bZ5Xl6ma9uLFtc1HwAiEeQBIBmGDBki6cmj6Jo1a2YdHhyT0NBQTZs2LcokRr6+vnE+O9owDOtjniwWS7RHjEVOOvbPP/+k+v21uXLlUr169SRJq1ativH53A8fPlT37t2TfazIHsS1a9fGODT577//Vu/evePcR58+feTs7CxJ+uijj+KchTvysWuR3nvvPevM/FOnTtXPP/8c67a3bt2KNolZtWrVJEmPHj3S1KlTo23z+PFjde3aNdGTn8Um8nxJsT/yasaMGXF+oGFjY6PPP/9c0pPz0b59ez169CjGthEREbp69Wqs+2rZsqXSp08vwzDUpk0bRUREyGKxqFOnTgl4NbHr1KmTdRKyTz/9VFeuXInW5tixY/ruu+8kPblXuVGjRsk6ZmxS4pzH5+2337bOED916tQYe9Rv3rypTz75JM799OzZ0/ohXPfu3RUYGBitzaZNm6zzUpQrV05vvfVWkutOiMhr299///1cj/OiJfeaDwCRCPIAkAzvv/++Pv74Y0lPhhsXLVpUI0aM0NatW+Xr66u9e/dq4cKF6tq1q3LkyKE+ffpE6anz9fVV6dKlVa5cOX3zzTdav369Dh8+rP3792vZsmWqXbu21q5dK+nJMPJnZwuvWLGipCdDhQcMGKDDhw/r3LlzOnfuXJKGfCfXxIkTrUGqefPm6tevn7Zv367Dhw9r4cKFKlu2rA4ePJjsENC+fXtJ0tWrV/X2229r3rx5OnjwoHbt2qXhw4erTJkyun37dpTh4M/Knj27ZsyYIenJ+StXrpw+/vhjbdy4Ub6+vtqzZ49mzpyp999/3xq8n7Zo0SKlS5dOERERat26tZo2baqVK1fq8OHDOnjwoJYuXaqOHTvKw8Mj2r3H9erVsz62bujQoRowYID27NkjHx8fLVy4UBUqVNCaNWusIz6Sq3Tp0tah0LNmzVLLli21bt06HT58WL///ruaN2+uXr16qVKlSnHup3fv3qpZs6YkafXq1SpRooQmT56svXv36ujRo9qwYYOGDRumIkWKaPbs2bHux9nZ2fqs7sh71WvUqJHsR/llzZpV48aNk/Tkw4YyZcro+++/18GDB+Xt7a2RI0eqcuXKun//viwWi2bPnh3nY+qSI6XOeXymTZsmGxsbhYaGqkaNGho6dKj27NmjQ4cOacaMGSpTpowuXbpkffxcTCMeSpQooU8//VTSkw86vLy89OOPP8rHx0c7d+7UZ599pvr16ys8PFx2dnaaNWtWsmpOiMhr26FDhzRmzBgdO3bMem2L6QMas0juNR8ArAwAQLwkGZKMatWqRVsXERFhjBgxwkibNq21XWxfzs7OxoMHD6zbzp8/P95tJBkVK1Y0/vvvv2jHDgoKMvLnzx/jNh4eHlHaenh4GJKMDh06RNvP9u3brdtt3749Qedi2LBhMa7ftGmT4ezsHOtrGTZsmDF06FBDkuHg4BDnsWLz6NEjo1atWrEew9HR0VixYoXRoUOHGM/F0xYsWGA4OjrGef5j297Hx8fInTt3vD+/8+fPR9t29+7dsZ6nNGnSGJMnTzaGDRtmXRaT+H4WTzt69KiRMWPGWGssUaKEcfXq1Xj3GRwcbDRr1ize1xxfTQcOHIjSfsmSJfG+hoQaNWqUYWNjE2tt9vb2xsKFC2PdPiHvm4RIiXP+9DUipveRYTx5D9va2sZ4jLRp0xo//vij8eGHHxqSjCJFisS4j/DwcKNXr15x/kxdXV2NP//8M8btE3MNMYz4z/Hly5eNTJkyxVjH09fh8+fPW5fPnz8/zmPGdQ2M73ctoXVHqlatWrRaDSP513wAiESPPAAkk8Vi0ddffy1/f38NHDhQZcuWtT7XOX369CpWrJjatm2rhQsXKiAgIMrkbK1bt9Yff/yhTz75RJUrV1a+fPnk5OQkOzs75cqVSw0bNtSSJUu0e/fuKLPkR0qXLp28vb318ccfq2jRovE+2/hFqFmzpk6ePKmPPvpIHh4esrOzU7Zs2VSvXj1t3LhRw4cPtw7ddXV1TdIxbG1ttX79ek2ZMkVly5aVk5OTHB0dVaBAAfXo0UNHjhxR8+bNE7SvDh066O+//9ZXX32lMmXKKEOGDEqTJo0yZsyoChUqaPDgwdq4cWOM25YpU0ZnzpzRlClTVKNGDbm5uSlt2rRKly6dSpQooe7du2vr1q0xDo+tXLmyDh8+rA8//FDu7u6ytbVVjhw51LRpU+3atUv9+vVL0rmJTalSpeTr66sePXrIw8NDtra2ypQpk8qVK6fx48fr4MGDCer9c3Jy0sqVK7Vt2zZ9+OGHypcvnxwdHWVnZ6fcuXOrQYMGmjVrlrWHNzblypVToUKFJEkZMmRQkyZNUuR1Sk9miT969Ki6deumN954Q46OjnJ2dlbRokX18ccf6/Tp09ZRHc9TSp3z+HTo0EE+Pj5q27at3N3dZWdnp5w5c6pFixbas2ePunbtGu/vnI2NjaZNm6Zdu3apbdu2ypMnj+zt7eXi4qJSpUpp8ODBOnv2rGrVqpXsehMiZ86cOnjwoLp06aICBQpY75k3u+Re8wEgksUwXvGH6wIAXjrvvfeetm7dqsqVK2v37t2pXQ5SQWBgoLJnz66HDx+qZ8+emj59emqX9EorUKCA/v77b7Vr1y7OifEAAOZAjzwA4IW6evWq9fFlKXUPOMxn2bJl1sn8unTpksrVvNoOHTpknTSO3zkAeDUQ5AEAKercuXOxrnv48KE6duxofRb1ixjejJdPWFiYJk6cKEkqW7as9QkASJq4fudu3bqlbt26SXryzPi4nmkPADCPtKldAADg1dK1a1cFBwerRYsWKlOmjDJlyqSgoCD5+Pho+vTp1tDRpUsXlShRIpWrxYty+/Zt3b59W7du3dKECRPk7+8v6cn97EiemjVrKl++fGrcuLFKliwpV1dX3blzR3v37tX06dMVEBAg6cnjMrNkyZLK1QIAUgL3yAMAUlT16tW1c+fOONs0btxYS5YsiTLxH15tw4cP14gRI6Isq1+/vvVRW0i6vHnzxvu4yV69emnq1KnW58UDAMyNHnkAQIqaOHGiVq9erW3btuny5cu6efOmDMOQm5ubKlSooA4dOuj9999P7TKRStKmTSsPDw+1bt1agwYNSu1yXgkLFy7U2rVrtWvXLgUEBOjmzZtKmzatsmfPrsqVK6t79+7W57IDAF4N9MjHICIiQlevXlX69OllsVhSuxwAAAAAwCvOMAwFBQXJ3d093hFU9MjH4OrVq8qdO3dqlwEAAAAAeM1cunRJuXLlirMNQT4G6dOnl/TkBLq4uKRyNQAAAACAV11gYKBy585tzaNxIcjHIHI4vYuLC0EeAAAAAPDCJOT2bqYuBQAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAACksEOHDqlFixZyd3eXra2tMmTIoCpVqmj+/PkyDMPa7sKFC7JYLPF+de7cOcHHrl69epz72rhxY7RtLly4oPr168vJyUlZs2ZV3759FRISEuP+9+3bJxsbG82aNSvxJwYpIm1qFwAAAAAAr5JffvlFLVu2VHh4uLy8vFSlShXdvHlTu3fv1p49e7RlyxYtWbJEkpQuXTp16NAh1n0tX75cISEhqlKlSqLraNq0qdKlSxdtec6cOaN8Hx4ervfff1+nT59W7dq1df36df3www96/PixZs6cGaVtRESEevfuLS8vL3Xr1i3RNSFlWIynPw6CJCkwMFCurq66d++eXFxcUrscAAAAACYRFhamnDlz6saNG1qyZInatGljXefn56fKlSvr9u3b2rZtm95555049+Xn56dixYrJ0dFR165dS3A2qV69unbu3Knz588rb9688bZfuXKlWrRooTFjxuiLL75QRESE6tatq23btunff/9Vjhw5rG1nzJih3r17y9vbWxUqVEhQPUiYxORQhtYDAAAAQAo5ffq0bty4ocKFC0cJ8ZJUtGhRtWvXTtKToffxWbx4sSTpgw8+eK4djL6+vpJkHRlgY2Ojjh07KiwsTKdOnbK2u3XrloYMGaKOHTsS4lMZQR4AAAAAUoi9vX2C2mXOnDnO9YZhaOnSpZKkDz/8MNl1xeXOnTuSpIwZM1qXRf47cp0kDR48WBERERozZsxzrQfx4x55AAAAAEgh+fPn1xtvvKEzZ85o6dKl0YbWL168WBkzZlTjxo3j3M+ePXt04cIFubm5qVatWkmqZe7cubp165ZsbGxUqFAhNWrUSHny5InWLnKZv7+/SpQoIUk6c+ZMlHU+Pj6aM2eOvv/+e7m5uSWpHqQc7pGPAffIAwAAAEiqvXv3qn79+rp79668vLxUsGBB3bhxQ7t371axYsW0YMEClS5dOs59fPTRR5o9e7b69eunyZMnJ+r4kffIP8vW1lZDhw7V0KFDoyw/duyYSpcurbp162r+/Pm6fv26GjRooLCwMJ07d0729vZ6++239fDhQx05ckRp0qRJVD1ImMTkUHrkAQAAACAFVapUSTt37lTjxo115MgRHTlyRJJkZ2enmjVrKn/+/HFuHxoaqpUrV0pK2rD6qlWrqmvXrqpYsaJy5MihS5cuadWqVfr222/19ddfy8XFRR9//LG1/ZtvvqmuXbvqxx9/VLZs2SRJFotFy5Ytk4ODg+bNm6cDBw5o165dUUL8w4cP5ejomOj6kHz0yMeAHnkAAAAASbVs2TJ16tRJFSpU0NixY+Xp6amrV69q/Pjxmj17try8vOTt7R3r/fS//vqrmjZtqiJFisjPzy/F6tq0aZNq166tDBky6OrVq1FCuGEYWrFihXbu3ClHR0e1bNlS5cqV0927d1W4cGHVqlVLixYtUkREhIYOHapp06bp3r178vDw0NixY9WiRYsUq/N1lZgcSpCPAUEeAAAAQFKcPXtWnp6ecnNz0+nTp6M9x71BgwZat26dpk+frp49e8a4jyZNmmj16tUaNWqUBg8enKL1vfXWW/Lx8dH27dtVvXr1eNv37dtXCxculL+/v7Jnz66JEyfq008/Vf/+/fXOO+9o5syZ+vPPP3X48GGVKlUqRWt93fD4OQAAAABIBT///LMeP36sOnXqRAvxkqw917t27Ypx+7t37+qPP/6QxWJR27ZtU7y+ggULSpICAgLibXv8+HHNmDFDw4cPV/bs2SVJ48ePV/Xq1TVp0iQ1bNhQy5cvl7Ozs8aPH5/itSJ2BHkAAAAASCGXL1+WJLm6usa4PnL50491e9qKFSsUGhqqKlWqyMPDI8Xrizyus7NzvG379OmjwoULq1+/fpKe9BgHBATorbfesrZJnz69ihQpor/++ivFa0XsmOwOAAAAAFJIZM+1j49PjOsPHTokScqbN2+M6xcvXizp+Tw7/ubNm9q9e7ckycvLK862S5Ys0e7du7V161alTRs1Nj548CDK98HBwUx694LRIw8AAAAAKeSDDz6Q9GTo/IwZM6Ks279/vyZNmiRJatasWbRtL168qD179sjBwUHNmzeP8zirV69WkSJF1L59+yjLvb299dtvvyk8PDzK8gsXLqhx48YKDg5Ww4YNlStXrlj3HRQUpM8//1wtWrRQjRo1rMtdXFyUK1curVmzRkFBQZKko0ePys/PT56ennHWi5RFjzwAAAAApBAvLy999tlnGj9+vHr16qVp06apWLFiunr1qvbt26eIiAh1795d7733XrRtlyxZIsMw1KBBg1iH5ke6d++ezpw5Yx0BEMnf31+dOnVS9uzZ5eXlpQwZMujixYs6fPiwQkJC5OnpqR9//DHOfY8YMUKBgYGaMGFCtHVffvml+vTpo5IlS6p06dLatm2bbGxsNHDgwAScHaQUgjwAAAAApKBx48apYsWKmjlzpg4fPqwzZ84offr0qlatmrp166bWrVvHuN2SJUskSe3atUvyscuXL6+ePXvqwIEDOnTokO7cuSNnZ2eVKlVKzZs3V8+ePeMcBu/n56cpU6Zo5MiRMfba9+rVS0FBQZo+fbrWrVunIkWKaPTo0fTIv2A8fi4GPH4OAAAAAPAi8fg5AAAAAABeUQR5AAAAAABMhCAPAAAAAICJMNkdAAAAAFMZYUntCmA2w16xmeHokQcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIA0A8Dh06pBYtWsjd3V22trbKkCGDqlSpovnz58sw4r7h6tGjRypWrJgsFovSpk3atCQPHz7U119/rUKFCsnBwUHu7u7q3Lmzrly5EmP7CxcuqH79+nJyclLWrFnVt29fhYSExNh23759srGx0axZs5JUGwAAAF48ixHfX6GvocDAQLm6uurevXtycXFJ7XIApKJffvlFLVu2VHh4uLy8vFSgQAHdvHlTu3fvVlhYmNq0aaMlS5bEuv3w4cM1cuRIGYahNGnSKCwsLFHHDwkJ0TvvvKP9+/crR44cqlKlii5cuKCDBw8qa9as2r9/v/Lnz29tHx4erhIlSuj06dOqXbu2rl+/rqNHj+qjjz7SzJkzo+w7IiJCZcuWlY2NjQ4ePCgbGz7bBQCYA5PdIbHMMNldYnIof7UBQCzCwsLUq1cvhYeHa8mSJTp8+LCWL1+ubdu26fjx48qUKZOWLl2q7du3x7i9n5+fRo8erW7duiW5hm+//Vb79+/X22+/LX9/fy1fvlwHDhzQhAkTdPPmTXXu3DlK+19//dV63A0bNsjHx0e1atXS3LlzFRAQEKXtrFmz5Ovrqx9++IEQDwAAYCL85QYAsTh9+rRu3LihwoULq02bNlHWFS1aVO3atZP0ZOj9swzDUPfu3ZUhQwaNGTMmScd/9OiRfvjhB0nStGnTlC5dOuu6AQMGqGTJktq5c6cOHz5sXe7r6ytJ6tChgyTJxsZGHTt2VFhYmE6dOmVtd+vWLQ0ZMkQdO3ZUhQoVklQfAAAAUgdBHgBiYW9vn6B2mTNnjrZs1qxZ2rNnjyZMmKCMGTMm6fh79+7VvXv39MYbb6h06dLR1jdr1kyStHbtWuuyO3fuSFKUY0b+O3KdJA0ePFgRERFJ/pABAAAAqYcgDwCxyJ8/v9544w2dOXNGS5cujbLOz89PixcvVsaMGdW4ceMo6wICAvTll1/q3XfftfbaJ8WxY8ckSV5eXjGuj1x+/Phx67I8efJIkvz9/a3Lzpw5E2Wdj4+P5syZo5EjR8rNzS3J9QEAACB1EOQBIBZp0qTRwoULlSFDBrVt21ZlypRRq1atVKNGDZUsWVK5cuXS1q1blSlTpijb9enTRyEhIZo+fXqyjv/vv/9KknLlyhXj+sjlFy9etC6rW7euLBaLvvzyS924cUMnTpzQpEmTlDNnTr355psyDEN9+vRR8eLF1atXr2TVBwAAgNSRtGchAcBrolKlStq5c6caN26sI0eO6MiRI5IkOzs71axZM8qM8ZL0+++/69dff9WwYcNUqFChZB37/v37kiQnJ6cY1zs7O0uSgoKCrMvefPNNde3aVT/++KOyZcsmSbJYLFq2bJkcHBw0b948HThwQLt27VKaNGms2z18+FCOjo7JqhcAAAAvBj3yABCHZcuWqVy5csqdO7cOHDig+/fvy9/fXx07dtSECRNUo0YNhYaGSnoSqPv06aNChQpp0KBBqVbzrFmz9PPPP6tnz54aMGCA9u/fr5YtW+ru3bsaNGiQ2rVrpypVqigiIkJfffWVMmTIICcnJ+XNm1crVqxItboBAACQMPTIA0Aszp49qw4dOsjNzU3r1q2zzhpfsGBBzZo1S1evXtW6des0b9489ezZU4MHD9bly5e1ZcuWBE+UF5fI4z148CDG9cHBwZKk9OnTR1lusVjUsmVLtWzZMsryoUOH6uHDhxo3bpwk6fvvv9d3332n/v3765133tHMmTPVunVrFSpUSKVKlUp2/QAAAHg+6JEHgFj8/PPPevz4serUqRPl0W+RWrRoIUnatWuXpCezxzs4OOibb75R9erVo3xJUnh4uPX7yMfExSVycrrLly/HuD5yuYeHR7z7On78uGbMmKHhw4cre/bskqTx48erevXqmjRpkho2bKjly5fL2dlZ48ePj3d/APAiHDp0SC1atJC7u7tsbW2VIUMGValSRfPnz5dhGNZ2jx8/1qZNm6xzgDg5OcnR0VFFixbVZ599pps3byb62NWrV5fFYon1a+PGjdG2uXDhgurXry8nJydlzZpVffv2VUhISIz737dvn2xsbDRr1qxE1wYA9MgDQCwig7Krq2uM6yOXP/1Yt5CQEO3cuTPWfUauu3v3brzHf/PNNyXJel/+syKXlyxZMt599enTR4ULF1a/fv0kSYGBgQoICIgyq3769OlVpEgR/fXXX/HuDwCet19++UUtW7ZUeHi4vLy8VKVKFd28eVO7d+/Wnj17tGXLFi1ZskTSk2tr7dq1JUl58+ZV3bp19fjxY+3bt08TJkzQkiVLtGPHDhUuXDjRdTRt2jTGD3Nz5swZ5fvw8HC9//77On36tGrXrq3r16/rhx9+0OPHjzVz5swobSMiItS7d295eXmpW7duia4JAAjyABCLyJ5rHx+fGNcfOnRI0pM/GqUnPTGxsVgsSpMmjcLCwhJ8/EqVKsnV1VV///23fH19ow13X7VqlSSpQYMGce5nyZIl2r17t7Zu3aq0aaNe9p8dth8cHMykdwBSXVhYmHr16qXw8HAtWbJEbdq0sa7z8/NT5cqVtXTpUnXt2lXvvPOObGxs1KJFC3366acqV66cte29e/fUsmVL/fnnn+rUqZO8vb0TXcv48eOt1/m4/Prrr/Lz89OYMWP0xRdfKCIiQnXr1tXcuXM1bNgw5ciRw9p21qxZ8vX1lbe3t2xsGCALIPG4cgBALD744ANJT4bOz5gxI8q6/fv3a9KkSZKkZs2aJes4q1evVpEiRdS+ffsoy+3s7NSnTx9JUu/eva33xEvSxIkTdfz4cVWrVk1lypSJdd9BQUH6/PPP1aJFC9WoUcO63MXFRbly5dKaNWuss94fPXpUfn5+8vT0TNbrAYDkOn36tG7cuKHChQtHCfGSVLRoUetoosgPVGvUqKHly5dHCfHSk5FT8+bNk/RkKPvTj+tMaZG3THXo0EGSZGNjo44dOyosLEynTp2ytrt165aGDBmijh07qkKFCs+tHgCvNoI8AMTCy8tLn332mSSpV69eKl68uFq0aKHKlSurUqVKCg4OVvfu3fXee+8l6zj37t3TmTNnrM+Nf9qQIUNUvnx5eXt7q2DBgmrZsqUqVKigTz/9VFmzZrX+gRqbESNGKDAwUBMmTIi27ssvv9SlS5dUsmRJNWnSxNqrNXDgwGS9HgBIroROGJo5c+Z427i7uytr1qySpKtXryarrrhE3maVMWNG67LIfz99C9bgwYMVERGhMWPGPLdaALz6CPIAEIdx48bp119/Va1atXTt2jWtXr1af/31l6pVq6alS5c+90mKHBwctH37dg0dOlROTk767bffdPHiRXXs2FFHjhyJ9hz7p/n5+WnKlCkaMmSIcuXKFW19r169NHr0aIWHh2vdunXKkyePfv/9d3rkAaS6/Pnz64033tCZM2e0dOnSKOv8/Py0ePFiZcyYUY0bN453X3fv3rUG6chbphJj7ty56tWrl/r06aMpU6bE+KGr9H8TlPr7+1uXnTlzJso6Hx8fzZkzRyNHjpSbm1uiawGASBbj6Sk/IenJJFCurq66d++eXFxcUrscAACA187evXtVv3593b17V15eXipYsKBu3Lih3bt3q1ixYlqwYIFKly4d735GjRqlIUOGqESJEjp+/HiCj1+9evUYJy+1tbXV0KFDNXTo0CjLjx07ptKlS6tu3bqaP3++rl+/rgYNGigsLEznzp2Tvb293n77bT18+FBHjhxRmjRpElwLohthSe0KYDbDTJB6E5NDmewOAAAAL51KlSpp586daty4sY4cOWJ9UoednZ1q1qwZ54ikSEePHtW3334rSfrf//6XqONXrVpVXbt2VcWKFZUjRw5dunRJq1at0rfffquvv/5aLi4u+vjjj63t33zzTXXt2lU//vijsmXLJunJRKfLli2Tg4OD5s2bpwMHDmjXrl1RQvzDhw+ZZBRAotEjHwN65AEAAFLXsmXL1KlTJ1WoUEFjx46Vp6enrl69qvHjx2v27Nny8vKSt7d3rPfTX79+XRUqVNCFCxfUv39/6wSlybVp0ybVrl1bGTJk0NWrV6OEcMMwtGLFCu3cuVOOjo5q2bKlypUrp7t376pw4cKqVauWFi1apIiICA0dOlTTpk3TvXv35OHhobFjx6pFixYpUuPrgB55JNar1iNPkI8BQR54cTz/+Se1S4DJnEpALxwAczt79qw8PT3l5uam06dPR3uOe4MGDbRu3TpNnz5dPXv2jLZ9UFCQqlevriNHjqh58+b6+eefU/Qxb2+99ZZ8fHy0fft2Va9ePd72ffv21cKFC+Xv76/s2bNr4sSJ+vTTT9W/f3+98847mjlzpv78808dPnw42qNGETOCPBLrVQvyTHYHAACAl8rPP/+sx48fq06dOtFCvCRrz/WuXbuirQsJCVHDhg115MgR1apVS4sXL07xZ7UXLFhQkhQQEBBv2+PHj2vGjBkaPny4dbK98ePHq3r16po0aZIaNmyo5cuXy9nZWePHj0/ROgG8ugjyAAAAeKlcvnxZ0pPnwMckcvnTj3WTpLCwMLVs2VI7duxQxYoV9euvv8rOzi7F64s8rrOzc7xt+/Tpo8KFC6tfv36SnvS4BQQE6K233rK2SZ8+vYoUKaK//vorxWsF8GpisjsAAAC8VCJ7rn18fGJcf+jQIUlS3rx5rcsMw1CnTp20Zs0alSpVSuvXr09Q0E6smzdvavfu3ZIkLy+vONsuWbJEu3fv1tatW5U2bdQ/ux88eBDl++DgYCa9A5Bg9MgDAADgpfLBBx9IejJ0fsaMGVHW7d+/3zpxXbNmzazL+/fvr8WLF6tIkSLatGmTMmTIEO9xVq9erSJFiqh9+/ZRlnt7e+u3335TeHh4lOUXLlxQ48aNFRwcrIYNGypXrlyx7jsoKEiff/65WrRooRo1aliXu7i4KFeuXFqzZo2CgoIkPZld38/PT56envHWDAASPfIAAAB4yXh5eemzzz7T+PHj1atXL02bNk3FihXT1atXtW/fPkVERKh79+567733JEm///67pkyZIknKnTu3Pv/88xj3++WXX6pIkSLW7+/du6czZ85YRwBE8vf3V6dOnZQ9e3Z5eXkpQ4YMunjxog4fPqyQkBB5enrqxx9/jPM1jBgxQoGBgZowYUKMdfTp00clS5ZU6dKltW3bNtnY2GjgwIGJOk8AXl8EeQAAALx0xo0bp4oVK2rmzJk6fPiwzpw5o/Tp06tatWrq1q2bWrdubW379L3ymzdvjnWfHTt2jBLkY1O+fHn17NlTBw4c0KFDh3Tnzh05OzurVKlSat68uXr27BnnMHg/Pz9NmTJFI0eOjLHXvlevXgoKCtL06dO1bt06FSlSRKNHj6ZHHkCC8fi5GPD4OeDF4fFzSCwePwcA4PFzSCwePwcAAAAAAFINQR4AAAAAABPhHnkAAIDXkYWxyUgk7sgFXhr0yAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiZgmyJ89e1atWrVSrly55OTkpCJFimjkyJF68OBBlHbe3t6qXLmynJyclD17dvXr10/3799PpaoBAAAAAEhZaVO7gIS4dOmSypUrJ1dXV/Xp00eZMmXSvn37NGzYMB0+fFi///67JMnX11fvvvuuihYtqokTJ+ry5csaP368zp49qw0bNqTyqwAAAAAAIPlMEeQXLVqku3fvas+ePfL09JQkde/eXREREfrpp590584dZcyYUYMHD1bGjBm1Y8cOubi4SJLy5s2rbt26adOmTapVq1ZqvgwAAAAAAJLNFEPrAwMDJUnZsmWLsjxHjhyysbGRnZ2dAgMDtXnzZrVr184a4iWpffv2SpcunVasWPFCawYAAAAA4HkwRZCvXr26JKlLly7y9fXVpUuXtHz5cs2YMUP9+vWTs7OzTpw4obCwMJUtWzbKtnZ2dipVqpSOHj0a6/5DQ0MVGBgY5QsAAAAAgJeRKYJ8nTp19M0332jz5s0qXbq08uTJo1atWqlv376aNGmSJCkgIEDSk176Z+XIkUNXr16Ndf+jR4+Wq6ur9St37tzP54UAAAAAAJBMpgjy0pN73atWrarZs2frl19+UefOnfXdd9/phx9+kCQ9fPhQkmRvbx9tWwcHB+v6mAwaNEj37t2zfl26dOn5vAgAAAAAAJLJFJPd/fzzz+revbv8/f2VK1cuSVKTJk0UERGhL774Qq1bt5ajo6OkJ8PknxUSEmJdHxN7e/sYPwAAAAAAAOBlY4oe+enTp6t06dLWEB+pYcOGevDggY4ePWodUh85xP5pAQEBcnd3fyG1AgAAAADwPJkiyF+/fl3h4eHRlj9+/FiSFBYWpuLFiytt2rTy8fGJ0ubRo0fy9fVVqVKlXkSpAAAAAAA8V6YI8oUKFdLRo0fl7+8fZfmyZctkY2OjkiVLytXVVe+9954WL16soKAga5tFixbp/v37at68+YsuGwAAAACAFGeKe+Q///xzbdiwQVWqVFGfPn2UOXNmrVu3Ths2bFDXrl2tw+ZHjRqlihUrqlq1aurevbsuX76sCRMmqFatWqpTp04qvwoAAAAAAJLPFD3yVatWlbe3t8qUKaPp06erf//++vvvvzVq1CjNmDHD2s7Ly0tbtmyRo6OjPvnkE82ePVtdunTRqlWrUrF6AAAAAABSjil65CWpXLly+uOPP+JtV7lyZe3du/cFVAQAAAAAwItnih55AAAAAADwBEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAF5BO3bskMViifdr5MiR1m3WrFmjDh06qESJEsqSJYtsbW3l5uam999/X+vWrUtSHeHh4Zo0aZJKlCghR0dHZc2aVS1atJCfn1+M7W/duqW2bdvKxcVFrq6u+vDDD3X79u0Y254/f16Ojo4aNGhQkmoDAMCs0qZ2AQAAIOVlz55dHTp0iHFdeHi4Fi9eLEmqUqWKdflPP/2kX3/9VZ6enipfvrzSp0+vCxcuaMOGDdqwYYMGDRqk7777LsE1REREqHnz5lq9erUyZMigevXq6b///tOqVau0fv16bd++XeXKlYuyTZs2bbRp0yZVrVpVhmFo8eLFunnzpjZu3Bht//3791eWLFk0ZMiQBNcEAMCrwGIYhpHaRbxsAgMD5erqqnv37snFxSW1ywFeaZ7//JPaJcBkTuXPn9olmN6GDRv0/vvvK3fu3Lp48aIsFosk6ejRo8qTJ48yZ84cpf2BAwf03nvvKTg4WMeOHVOJEiUSdJw5c+aoW7duKliwoHbv3q1s2bJJkn755Rc1a9ZMBQoUkJ+fn9KmfdKvcOjQIZUrV049evTQjBkzJEndunXTnDlzdOjQIZUtW9a6740bN6pu3bpavny5WrRokexz8lr6/z93IMFeotgwgrcvEmnYy/P2jVVicihD6wEAeM1E9sa3bdvWGuIlqXTp0tFCvCSVL19eLVu2lGEY2r59e4KPM3HiREnS2LFjrSFekpo2baqGDRvq3Llz+v33363LfX19JSnKSILOnTtHWSdJjx49Ur9+/VSjRg1CPADgtUSQBwDgNRIcHGwNzx9++GGCt7O1tZUk2dnZJaj9+fPn5efnJ0dHR9WrVy/a+mbNmkmS1q5da112584dSVLGjBmtyyL/HblOkiZMmKDz589r6tSpCa4fAIBXCUEeAIDXyK+//qrg4GCVLl1axYoVS9A2J06c0PLly2Vra6uaNWsmaJtjx45JkooXL279EOBpXl5ekqTjx49bl+XJk0eS5O/vb1125syZKOsuXbqkUaNGqV+/fgmuHwCAVw1BHs9VYmdNjoiI0O7duzVw4ECVKVNG6dOnl729vd544w316NFD58+fT1IdzJoMAE9EDquPqzd+7dq16tixo9q2basqVaqoVKlSevDggX788Ue98cYbCTrOv//+K0nKlStXjOsjl1+8eNG6rHr16nJ0dNSIESN08eJFXbhwQSNGjJCTk5OqV68uSfr000/l4uKi4cOHJ6gOAABeRcxaj+cqsbMm//PPP6patap12xo1aihNmjQ6ePCgZs2apaVLl+qPP/5Q5cqVE1wDsyYDwBMBAQHaunWr0qRJo9atW8fa7tixY1q4cKH1e0dHR02ePDlRQ/Hv378vSXJycopxvbOzsyQpKCjIuix79uwaMmSIvvrqK+XNm9e6/H//+5+yZcumbdu2aeXKlVq0aJHSp09vXf/gwYNYjwMAwKuIII/nqkiRIlqwYEGM6zZs2KDFixcrd+7c1p4Wi8WimjVr6ssvv9Q777xjnYQpNDRUPXr00IIFC9S2bVudO3cuxqGaMZk3b55Wr14d66zJbdu2jTZr8qZNm2KcNdnHxyfarMlr1qzR8uXLrX+UAsDLatmyZQoPD1edOnWUPXv2WNsNGTJEQ4YMUUhIiM6dO6cZM2aoe/fuWrNmjX755ZcE3yefFIMHD5aXl5fWr18vi8WiBg0aqGbNmgoLC1Pfvn1VpUoVtWvXTpI0efJkfffdd7px44bc3Nw0ZMgQ9e3b97nVBgDAy4Kh9Ug1Mc2a/MYbb2jTpk2qUaNGlJmU7e3tNX36dLm6uurff/+Vt7d3go/DrMkA8ERChtU/zcHBQcWLF9e0adPUt29frVu3LsETzKVLl07Sk97ymAQHB0tSlJ71SHXq1NHUqVM1ZcoU6z35kydP1pkzZ/TDDz9IenKvf//+/VWrVi39/vvvql27tvr166c1a9YkqD4AAMyMII9UkZRZkx0dHVWoUCFJ0tWrVxO0DbMmA8ATfn5+Onr0qNKlS6dGjRolevvIa/XTH3zGJXJyusuXL8e4PnK5h4dHvPu6du2aRowYoV69eqlkyZKSpPHjxyt//vxauHChGjZsqAULFihfvnwaO3ZsguoDAMDMCPJIFUmZNTkiIsI6KVJcQ0KfxqzJAPDEokWLJElNmjRJ0v3kWbJkkSTdvHkzQe3ffPNNSdLJkyf1+PHjaOuPHDkiSdZgHpfPP/9cjo6O1olRJen06dMqU6aMbGye/CljY2OjsmXL6q+//kpQfQAAmBlBHqkiscM7pSf3dt64cUNZs2ZVxYoVE7QNsyYDgGQYhpYuXSopcdfdp+3cuVOSEjxrfb58+VS0aFE9fPhQ69evj7Z+1apVkqQGDRrEuZ89e/Zo8eLFGjNmjDJkyBBl3bPD9oODg63BHgCAVxn/t8MLl9BZk5926dIl9e/fX5I0cuRI2dvbJ2i75MyafPjwYeXNm1f58uXT0aNHNWzYsCizJo8dOzbarMkA8DLavXu3Ll68qJw5c6pGjRoxtrl586Z+/PHHGK9lmzdv1sCBAyVJnTp1irLu4MGDKlKkiN59991o2w0YMECSNHDgQN24ccO6/Ndff9WaNWtUoEABffDBB7HWHR4erj59+qhChQrq2LFjlHWenp7asWOHrly5Ikm6cuWKdu7cKU9Pz1j3BwDAq4JZ6/HCJXTW5EjBwcFq0qSJ/vvvPzVq1Eg9evR47jUyazKAV0nkKKg2bdrE2mMdHBys7t27q3///ipTpoxy5cql4OBg+fv76/Tp05KkTz75RE2bNo2y3YMHD3TmzBmFhIRE22fnzp31xx9/aPXq1daw/99//2nnzp1ydHTU4sWLrU8MicmMGTN04sQJHTx4MMoEqJL05Zdfqn79+ipTpowqVaqkvXv3Kjg4WIMGDUrUuQEAwIwI8njhEjOs/vHjx2revLl8fHxUuXJl69DQhErurMl16tSJsixy1uTIezsjZ01u166dmjdvrlWrVqlfv37y8PBQw4YNE1UrADwPoaGh1mHskR9AxsTNzU1jx47Vjh07dOrUKfn4+CgiIkI5cuRQq1at9NFHH1lvL0ooGxsbrVy5UpMnT9a8efO0bt06OTs7q2nTphoxYkScc4z8999/+vrrr9WtWzeVKVMm2vp69epp7ty5GjNmjNauXSsPDw+NGTMm2nUbAIBXkcUwDCO1i3jZBAYGytXVVffu3ZOLi0tql/NK8fPzU7FixZQuXTpdv349zgmXIiIi1K5dOy1btkylSpXS9u3bo90fGZ/ffvtNjRs31ltvvaWDBw9GW3/q1CkVL15cpUuXtobz2Fy7dk2FChVSx44dNWXKFElSxYoVdf36dZ09e1Y2NjaKiIhQgQIF5O7urj179iSq1teV5z//pHYJMJlT+fOndgnAq+GZUQ5AvF6i2DCCty8SadjL8/aNVWJyKPfI44VKzKzJffv21bJly1SoUCH9+eefiQ7xErMmAwAAAHj1EOTxwiRm1uQhQ4Zo+vTpypMnjzZv3iw3N7ckHZNZkwEAAAC8arhHHi9MQmZNlqRJkyZp1KhRyp49u7Zs2WJ9dntcDh48qPbt2ytnzpzaunVrlHUDBgxQt27dNHDgQFWsWNH6oUBKz5qcM2dO66zJMd3PCeDVc+qfEaldAkzGM/+w1C4BAPAKIMjjhUnIrMm+vr769NNPJT3pTR81alSM7bp27arKlStbv2fWZAAAAACvC4I8XoiEzpp89+5dRc6/uG/fPu3bty/GdtWrV48S5OPCrMkAAAAAXiXMWh8DZq0HXhxmrUdivUyz1jO0Hon1Ug2tZ9Z6JNZLFBuYtR6Jxaz1AAAAAAAg1RDkAQAAAAAwEYI8AAAAAAAmwmR3rwDuEUJimeEeIQAAAAAxo0ceAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAipgryR44cUcOGDZUpUyY5OTmpePHimjJlSpQ23t7eqly5spycnJQ9e3b169dP9+/fT6WKAQAAAABIWWlTu4CE2rRpkxo0aKDSpUtr6NChSpcunf7++29dvnzZ2sbX11fvvvuuihYtqokTJ+ry5csaP368zp49qw0bNqRi9QAAAAAApAxTBPnAwEC1b99e9erV06pVq2RjE/NAgsGDBytjxozasWOHXFxcJEl58+ZVt27dtGnTJtWqVetFlg0AAAAAQIozxdD6pUuX6vr16xo1apRsbGwUHBysiIiIKG0CAwO1efNmtWvXzhriJal9+/ZKly6dVqxY8aLLBgAAAAAgxZkiyG/ZskUuLi66cuWKChcurHTp0snFxUU9e/ZUSEiIJOnEiRMKCwtT2bJlo2xrZ2enUqVK6ejRo7HuPzQ0VIGBgVG+AAAAAAB4GZkiyJ89e1ZhYWH64IMPVLt2bf3yyy/q3LmzZs6cqU6dOkmSAgICJEk5cuSItn2OHDl09erVWPc/evRoubq6Wr9y5879fF4IAAAAAADJZIp75O/fv68HDx6oR48e1lnqmzRpokePHmnWrFkaOXKkHj58KEmyt7ePtr2Dg4N1fUwGDRqkAQMGWL8PDAwkzAMAAAAAXkqm6JF3dHSUJLVu3TrK8jZt2kiS9u3bZ20TGhoabfuQkBDr+pjY29vLxcUlyhcAAAAAAC8jUwR5d3d3SVK2bNmiLHdzc5Mk3blzxzqkPnKI/dMCAgKs+wAAAAAAwMxMEeTLlCkjSbpy5UqU5ZH3vWfNmlXFixdX2rRp5ePjE6XNo0eP5Ovrq1KlSr2QWgEAAAAAeJ5MEeRbtGghSZo7d26U5XPmzFHatGlVvXp1ubq66r333tPixYsVFBRkbbNo0SLdv39fzZs3f6E1AwAAAADwPJhisrvSpUurc+fOmjdvnsLCwlStWjXt2LFDK1eu1KBBg6zD5keNGqWKFSuqWrVq6t69uy5fvqwJEyaoVq1aqlOnTiq/CgAAAAAAks8UQV6SZs6cqTx58mj+/PlavXq1PDw8NGnSJPXv39/axsvLS1u2bNEXX3yhTz75ROnTp1eXLl00evTo1CscAAAAAIAUZJogb2trq2HDhmnYsGFxtqtcubL27t37gqoCAAAAAODFMsU98gAAAAAA4AmCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEwkyUF+165dKVkHAAAAAABIgCQH+erVq6tYsWL6/vvvdfv27ZSsCQAAAAAAxCJZQ+tPnz6tTz/9VLly5dKHH35ILz0AAAAAAM9ZkoP82bNnNXDgQLm5uSkkJERLly7VO++8Qy89AAAAAADPUZKD/BtvvKExY8bo0qVLWrVqlWrWrCmLxUIvPQAAAAAAz1GyZ61PmzatmjRpoo0bN+qff/7RV199JXd3d4WEhGjJkiX00gMAAAAAkIJS9PFzefLk0TfffKOLFy/q999/V4MGDWRjYxOtl37v3r0peVgAAAAAAF4bz+U58jY2NmrQoIF69uyp8uXLy2KxyDAMay991apVValSJR06dOh5HB4AAAAAgFdWigf5gIAAffvtt8qfP7/q1asnb29vGYahypUra/LkyapXr54sFov27dunypUra8eOHSldAgAAAAAAr6wUCfKGYWj9+vVq1KiRPDw8NGzYMF24cEHp06dX7969deLECe3atUt9+/bV2rVr5e/vr5o1a+rx48caOnRoSpQAAAAAAMBrIW1yNr58+bLmzp2refPm6fLlyzIMQ5Lk5eWlHj16qE2bNnJycoq2Xf78+bVy5Uq5ubnp+PHjySkBAAAAAIDXSpKDfP369fXnn38qIiJChmHIyclJLVu2VI8ePfTWW2/Fu72Li4uyZ8+uS5cuJbUEAAAAAABeO0kO8n/88YckqWjRovroo4/UoUMHubq6JmofzZo1061bt5JaAgAAAAAAr50kB/nI3vdq1aol+eDjx49P8rYAAAAAALyOkhzkly1blpJ1AAAAAACABHguz5EHAAAAAADPR5KD/P79++Xl5aXevXvH27Zr167y8vKSj49PUg8HAAAAAACUjCC/dOlSHTt2TFWqVIm3bYUKFeTr66ulS5cm9XAAAAAAAEDJCPI7d+6UJNWqVSveto0bN5Ykbd++PamHAwAAAAAASkaQv3z5slxdXZUpU6Z422bOnFmurq66cuVKUg8HAAAAAACUjCD/8OFDRUREJLi9YRgKCgpK6uEAAAAAAICSEeTd3NwUFBSkq1evxtv2ypUrCgwMVJYsWZJ6OAAAAAAAoGQE+QoVKkiSpk2bFm/byDbly5dP6uEAAAAAAICSEeS7dOkiwzA0duxYzZ49O9Z2s2bN0tixY2WxWNSlS5ekHg4AAAAAAEhKm9QNa9asqWbNmmnVqlXq2bOnpk2bpvr168vDw0OSdPHiRa1du1anTp2SYRhq2rSp6tatm2KFAwAAAADwOkpykJekhQsXymKxaOXKlTpx4oROnjwZZb1hGJKkVq1aae7cuck5FAAAAAAAUDKG1kuSo6Ojli9fri1btqhNmzby8PCQvb29HBwclDdvXrVt21bbtm3T0qVL5ejomFI1AwAAAADw2kpWj3ykGjVqqEaNGimxKwAAAAAAEIdk9cgDAAAAAIAXiyAPAAAAAICJpMjQ+kePHsnX11eXL19WcHCwdZK7mLRv3z4lDgkAAAAAwGspWUE+NDRUX331lWbPnq3g4OB421ssFoI8AAAAAADJkOQgHxYWptq1a2v37t0yDENubm66ceOGbGxs5O7urv/++08hISGSpHTp0ilz5swpVjQAAAAAAK+rJN8jP3fuXO3atUvu7u7y8fHRtWvXJElubm76999/df/+fW3fvl0VK1ZUWFiYvv32W50/fz7FCgcAAAAA4HWU5CC/bNkyWSwWjRo1Sl5eXtF3bGOjatWqaefOnapcubI6d+6sI0eOJKtYAAAAAABed0kO8idPnpQkNWvWLMry8PDwKN+nSZNGEydO1OPHjzV+/PikHg4AAAAAACgZQT4oKEiurq5ycnKyLrOzs9P9+/ejtS1evLjSp0+v3bt3J/VwAAAAAABAyQjybm5u0XrfM2fOrJCQEN24cSPKcsMw9OjRI928eTOphwMAAAAAAEpGkM+VK5fu37+vu3fvWpcVL15ckrRx48YobXfs2KHQ0FC5urom9XAAAAAAAEDJCPJvvfWWJMnb29u6rHHjxjIMQ5999plWrlyps2fPatWqVerQoYMsFotq1KiR/IoBAAAAAHiNJTnIN2rUSIZh6Oeff7Yu69Kli4oXL67//vtPrVq1UpEiRdSyZUtdvnxZzs7OGjZsWIoUDQAAAADA6yrJQf6dd97R+fPnNXr0aOsyW1tbbd26Va1bt5a9vb0Mw5AkVa5cWTt27FCRIkWSXzEAAAAAAK+xtEnd0GKxyMPDI9ryrFmzasmSJQoLC9PNmzfl4uIiZ2fnZBUJAAAAAACeSHKQ37VrlySpZMmSypAhQ/Qdp02rHDlyJLkwAAAAAAAQXZKDfPXq1ZUmTZpoj5oDAAAAAADPT5KDvKurq9KkSaOMGTOmZD0AAAAAACAOSZ7srkCBAgoKClJoaGhK1gMAAAAAAOKQ5CDfqlUrPX78WCtWrEjJegAAAAAAQBySHOQ//vhjvf322+rTp4/++OOPlKwJAAAAAADEIsn3yH/33XeqWrWqTpw4oQYNGsjT01OVKlWSm5ub0qRJE+t2X3/9dVIPCQAAAADAay/JQX748OGyWCwyDEOSdPLkSZ06dSre7QjyAAAAAAAkXZKDfNWqVWWxWFKyFgAAAAAAEI8kB/kdO3akYBkAAAAAACAhkjzZHQAAAAAAePEI8gAAAAAAmAhBHgAAAAAAE0nyPfI1atRI9DYWi0Vbt25N6iEBAAAAAHjtPffJ7iJntjcMg1nuAQAAAABIpiQH+WHDhsW5/t69ezpw4ID27dunzJkzq2fPnkqTJk1SDwcAAAAAAPQcg3ykbdu2qUmTJvrrr7+0atWqpB4OAAAAAADoBUx2V6NGDU2ePFmrV6/WnDlznvfhAAAAAAB4pb2QWetbtmypNGnSEOQBAAAAAEimFxLkHRwc5OzsLD8/vxdxOAAAAAAAXlkvJMhfuXJF9+7dk2EYL+JwAAAAAAC8sp57kH/48KF69eolSSpRosTzPhwAAAAAAK+0JM9aP3LkyDjXh4SE6NKlS/rzzz9169YtWSwW9e7dO6mHAwAAAAAASkaQHz58uCwWS7ztDMOQjY2NhgwZojZt2iT1cAAAAAAAQMkI8lWrVo0zyKdNm1YZM2bUm2++qRYtWqhgwYJJPRQAAAAAAPj/khzkd+zYkYJlAAAAAACAhHghs9YDAAAAAICUQZAHAAAAAMBEkhzkL1y4oAEDBmjy5Mnxtp0wYYIGDBigS5cuJfVwAAAAAABAyQjyixYt0uTJk2UYRrxtHzx4oMmTJ2vx4sVJPRwAAAAAAFAygvyGDRskSY0aNYq3bdu2bWUYhtavX5/UwwEAAAAAACVzaL2Tk5Py5s0bb9v8+fPLyclJFy9eTOrhAAAAAACAkhHkb9++LXt7+wS3d3Bw0M2bN5N6OAAAAAAAoGQE+QwZMuju3bsKCgqKt21QUJDu3r0rFxeXpB4OAAAAAAAoGUG+dOnSMgxDK1eujLft8uXLFRERoRIlSiT1cAAAAAAAQMkI8k2bNpVhGBo4cKCOHz8ea7tjx47piy++kMViUYsWLZJ6OAAAAAAAoGQE+Q4dOsjT01O3b99WhQoV9PHHH2vz5s3y9/eXv7+/Nm/erH79+untt9/WnTt3VKxYMXXp0iUlawcAAAAA4LWTNqkb2traas2aNapdu7bOnTunH374QT/88EO0doZhqGDBglq7dq3Spk3y4QAAAAAAgJLRIy9J+fLl0+HDh/XVV18pR44cMgwjype7u7u+/vprHT58OEGPqQMAAAAAAHFLdhd5+vTp9c033+ibb77Rv//+q2vXrslisSh79uzKnTt3StQIAAAAAAD+vxQd654nTx7lyZMnJXcJAAAAAACekqyh9QAAAAAA4MVKcpDfv3+/vLy81Lt373jbdu3aVV5eXvLx8Unq4QAAAAAAgJIR5JcuXapjx46pSpUq8batUKGCfH19tXTp0qQeDgAAAAAAKBlBfufOnZKkWrVqxdu2cePGkqTt27cn9XAAAAAAAEDJCPKXL1+Wq6urMmXKFG/bzJkzy9XVVVeuXEnq4QAAAAAAgJIR5B8+fKiIiIgEtzcMQ0FBQUk9XBSjRo2SxWJR8eLFo63z9vZW5cqV5eTkpOzZs6tfv366f/9+ihwXAAAAAIDUluQg7+bmpqCgIF29ejXetleuXFFgYKCyZMmS1MNZXb58Wd99952cnZ2jrfP19dW7776rBw8eaOLEieratatmz56t5s2bJ/u4AAAAAAC8DJL8HPkKFSro0qVLmjZtmkaNGhVn22nTpkmSypcvn9TDWX322WeqUKGCwsPD9d9//0VZN3jwYGXMmFE7duyQi4uLJClv3rzq1q2bNm3alKD7+QEAAAAAeJkluUe+S5cuMgxDY8eO1ezZs2NtN2vWLI0dO1YWi0VdunRJ6uEkSbt27dKqVav0/fffR1sXGBiozZs3q127dtYQL0nt27dXunTptGLFimQdGwAAAACAl0GSe+Rr1qypZs2aadWqVerZs6emTZum+vXry8PDQ5J08eJFrV27VqdOnZJhGGratKnq1q2b5ELDw8PVt29fde3aVSVKlIi2/sSJEwoLC1PZsmWjLLezs1OpUqV09OjRWPcdGhqq0NBQ6/eBgYFJrhMAAAAAgOcpyUFekhYuXCiLxaKVK1fqxIkTOnnyZJT1hmFIklq1aqW5c+cm51CaOXOmLl68qC1btsS4PiAgQJKUI0eOaOty5Mih3bt3x7rv0aNHa8SIEcmqDwAAAACAFyHJQ+slydHRUcuXL9eWLVvUpk0beXh4yN7eXg4ODsqbN6/atm2rbdu2aenSpXJ0dEzycW7duqWvv/5aQ4cOVdasWWNs8/DhQ0mSvb19tHUODg7W9TEZNGiQ7t27Z/26dOlSkmsFAAAAAOB5SlaPfKQaNWqoRo0asa6PiIjQ+vXrNXfuXP3222+J3v+QIUOUKVMm9e3bN9Y2kR8UPD1EPlJISEicHyTY29vH+AEAAAAAAAAvmxQJ8rE5e/as5s6dq59++knXr19P8j5mz56t77//Psqj7kJCQvT48WNduHBBLi4u1iH1kUPsnxYQECB3d/ekvQgAAAAAAF4iyRpaH5MHDx5owYIFqlKliooUKaJx48bp2rVrMgxDRYoUSfT+rly5ooiICPXr10/58uWzfh04cED+/v7Kly+fRo4cqeLFiytt2rTy8fGJsv2jR4/k6+urUqVKpdArBAAAAAAg9aRYj/z+/fs1d+5crVixQvfv35cka3hv3ry5mjdvruLFiyd6v8WLF9fq1aujLR8yZIiCgoI0efJkvfHGG3J1ddV7772nxYsXa+jQoUqfPr0kadGiRbp//76aN2+evBcIAAAAAMBLIFlB/ubNm/rpp580b948nT59WtL/zVRvsVh06NAhlSlTJlkFZsmSRY0aNYq2PPJZ8k+vGzVqlCpWrKhq1aqpe/fuunz5siZMmKBatWqpTp06yaoDAAAAAICXQaKH1huGofXr16tp06bKlSuXBg4cKD8/Pzk4OKhVq1bauHGjtW3RokVTtNj4eHl5acuWLXJ0dNQnn3yi2bNnq0uXLlq1atULrQMAAAAAgOclwT3yf//9t+bNm6eFCxcqICBAhmHIYrGocuXKat++vVq0aGEdzv4i7NixI8bllStX1t69e19YHQAAAAAAvEgJDvIFCxaUxWKRYRjKly+f2rdvr/bt2ytfvnzPsz4AAAAAAPCURN8j369fP40dO1Z2dnbPox4AAAAAABCHBN8jb29vL8MwNHXqVLm7u6t3797av3//86wNAAAAAAA8I8FBPiAgQFOmTFHJkiV1+/ZtzZgxQ5UqVVLhwoX13Xff6d9//32edQIAAAAAACUiyGfIkEF9+vTR0aNHdfjwYfXs2VOurq46e/ashg4dqvz586tGjRqaP3/+86wXAAAAAIDXWqIfPydJpUuX1rRp0xQQEKBFixapWrVqMgxDO3bsUNeuXa3tNm3apLCwsBQrFgAAAACA112Sgnwke3t7tW3bVtu2bdO5c+f01VdfKWfOnJKePG++adOmcnNzU6dOnfTHH38Q6gEAAAAASKZkBfmn5cuXT998840uXryoP/74Q02aNFHatGl19+5d/fTTT2rQoIGyZcuWUocDAAAAAOC1lGJBPpLFYlGdOnW0atUqXblyRePHj1fRokVlGIbu3r2b0ocDAAAAAOC1kuJB/mlZsmTRgAEDdPLkSXl7e6tLly7P83AAAAAAALzy0r6oA1WoUEEVKlR4UYcDAAAAAOCV9Fx75AEAAAAAQMoiyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEVME+UOHDqlPnz7y9PSUs7Oz8uTJoxYtWsjf3z9aWz8/P9WpU0fp0qVTpkyZ9OGHH+rmzZupUDUAAAAAACkvbWoXkBD/+9//tHfvXjVv3lwlS5bUtWvX9MMPP8jLy0v79+9X8eLFJUmXL19W1apV5erqqu+++07379/X+PHjdeLECR08eFB2dnap/EoAAAAAAEgeUwT5AQMGaOnSpVGCeMuWLVWiRAmNGTNGixcvliR99913Cg4O1uHDh5UnTx5JUrly5VSzZk0tWLBA3bt3T5X6AQAAAABIKaYYWl+xYsVovekFCxaUp6en/Pz8rMt++eUX1a9f3xriJem9995ToUKFtGLFihdWLwAAAAAAz4spgnxMDMPQ9evXlSVLFknSlStXdOPGDZUtWzZa23Llyuno0aOx7is0NFSBgYFRvgAAAAAAeBmZNsgvWbJEV65cUcuWLSVJAQEBkqQcOXJEa5sjRw7dvn1boaGhMe5r9OjRcnV1tX7lzp37+RUOAAAAAEAymDLInz59Wr1799bbb7+tDh06SJIePnwoSbK3t4/W3sHBIUqbZw0aNEj37t2zfl26dOk5VQ4AAAAAQPKYYrK7p127dk316tWTq6urVq1apTRp0kiSHB0dJSnGXveQkJAobZ5lb28f4wcAAAAAAAC8bEwV5O/du6e6devq7t272r17t9zd3a3rIofURw6xf1pAQIAyZcpEWAcAAAAAmJ5pgnxISIgaNGggf39/bdmyRcWKFYuyPmfOnMqaNat8fHyibXvw4EGVKlXqBVUKAAAAAMDzY4p75MPDw9WyZUvt27dPK1eu1Ntvvx1ju6ZNm2rdunVR7nHfunWr/P391bx58xdVLgAAAAAAz40peuQ//fRTrVmzRg0aNNDt27e1ePHiKOvbtWsnSRo8eLBWrlypd955Rx9//LHu37+vcePGqUSJEurUqVNqlA4AAAAAQIoyRZD39fWVJK1du1Zr166Ntj4yyOfOnVs7d+7UgAED9OWXX8rOzk716tXThAkTuD8eAAAAAPBKMEWQ37FjR4Lbenp66s8//3x+xQAAAAAAkIpMcY88AAAAAAB4giAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIq9ckA8NDdUXX3whd3d3OTo6qnz58tq8eXNqlwUAAAAAQIp45YJ8x44dNXHiRLVt21aTJ09WmjRp9P7772vPnj2pXRoAAAAAAMmWNrULSEkHDx7Uzz//rHHjxumzzz6TJLVv317FixfXwIED5e3tncoVAgAAAACQPK9Uj/yqVauUJk0ade/e3brMwcFBXbp00b59+3Tp0qVUrA4AAAAAgOR7pXrkjx49qkKFCsnFxSXK8nLlykmSfH19lTt37mjbhYaGKjQ01Pr9vXv3JEmBgYHPsdqUE5LaBcB0Xqa3dnhQUGqXAJN5ma7N94O4AiNxXqb3L5BoL9H7l6svEuslevvGKvL/EYZhxNv2lQryAQEBypEjR7TlkcuuXr0a43ajR4/WiBEjoi2PKfQDr4IxrqldAZB0vH1hbmNSuwAg6Vy5AsO8zPT3b1BQkFzj+X17pYL8w4cPZW9vH225g4ODdX1MBg0apAEDBli/j4iI0O3bt5U5c2ZZLJbnUyyeq8DAQOXOnVuXLl2KNkIDeNnx/oWZ8f6F2fEehpnx/jU3wzAUFBQkd3f3eNu+UkHe0dExyhD5SCEhIdb1MbG3t4/2AUCGDBlSvD68eC4uLlzEYFq8f2FmvH9hdryHYWa8f80rvp74SK/UZHc5cuRQQEBAtOWRyxLyyQYAAAAAAC+zVyrIlypVSv7+/tEmkjlw4IB1PQAAAAAAZvZKBflmzZopPDxcs2fPti4LDQ3V/PnzVb58eSave43Y29tr2LBhMc6ZALzseP/CzHj/wux4D8PMeP++PixGQua2N5EWLVpo9erV+uSTT1SgQAEtXLhQBw8e1NatW1W1atXULg8AAAAAgGR55YJ8SEiIhg4dqsWLF+vOnTsqWbKkvvnmG9WuXTu1SwMAAAAAINleuSAPAAAAAMCr7JW6Rx4AAAAAgFcdQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAALykIuelfvToUZT/Aq+S8PBwSVJwcHAqV2IeBHnABCIiIlK7BOCF4WEqAPDExYsXtXHjRt26dUt2dnY6efKkevTooX///Te1SwNSjGEYSpMmjY4fP66BAwfqzJkzqV2SKRDkgZdceHi4bGye/KqePn1aN2/eTOWKgOcnPDxcFovF+u/w8HBrsCfgIyEie3XCwsIUFhaWytUAybN582Z17txZU6dOlbe3t8qWLas7d+4oTZo0qV0akCIMw5DFYtHdu3fVtGlT+fr6Wq/jiBvPkQdeYuHh4db/WXfr1k1+fn764IMP1L9/f9na2qZydUDKevr9PmLECB07dkz37t1TgQIFNGzYMLm7u6dyhXjZRb6H/P399c033+jUqVOys7PTO++8o169eil37typXSKQKGFhYWrQoIF27twpwzBUpUoVzZ49Wx4eHtYPPQGzioiIkI2NjW7fvq2jR49q6NChGjVqlN55553ULs0U6JEHXlKRw4wkqX79+tq4caOqVKmiVq1aEeLxynn6/f7+++/r+++/1507d2QYhtatW6fSpUtr27ZtqVwlXmaR7yE/Pz9VrFhRvr6+Kly4sDJmzKiFCxeqYsWKunDhQmqXCSRYeHi40qZNq7FjxyokJESGYcjDw0MZM2aUxWKh1xKmZ2Njo6tXr6po0aL69ttvFRwcbA3x3FYaP4I88JKK/KR9yJAh8vHx0cSJEzVo0KAoPUqPHz+WxJBjmF/k+33YsGE6evSofvzxR/3555/atm2b+vfvr5s3b2rPnj1M8oRYWSwWBQUFqWfPnvL09NSCBQu0bNkybdiwQYULF9a1a9d09OjR1C4TSJCnP9y8deuWateurVKlSmnRokUaO3asdXg9YQdm5+7urhIlSmjnzp36559/dO7cOUlixEkCEOSBl8zT/1N++PChvL299dZbb6lZs2ZycXHRrVu3tHXrVvXt21f9+/fXtm3buNjhlXHkyBFVqFBBNWvWlJ2dnfbs2aPhw4erU6dO6tChg+zs7Kxt+QMWz7p9+7bOnTun5s2bq0yZMpKkr776Snv27NGPP/6od999VxLvHbzcIucKuX37tq5evarq1atrw4YN2r9/v6pXr64JEyZo7NixunfvnnUOHen/PtwHXnbPPolhy5YtateunYKDgzVq1Chdu3ZNFouFjqp4EOSBl8jTE9tdvXpVjo6OCg8PV1BQkK5du6aTJ0+qTZs2atWqlRYuXKglS5bo/fff1969e1O5ciD5goODdfLkSRUoUECurq7asWOHateurUaNGunbb7+Vh4eHJGnhwoUKCgqK8gcsIEn37t3T3bt3VbRoUUnSwIEDNW7cOE2fPl0tW7aUi4uLwsLCNG3aNAUGBqZytUB0kfM8nDlzRo0aNVKTJk107tw568SNGzZsULVq1TRp0iSNGTPG+qius2fPasqUKTp9+nRqlg/EKfJ2kJhuC/npp5/UuHFjLVy4UBMnTtSNGzcI8/HgryDgJRI5jO7tt99W3bp19eDBA9WrV09HjhxRiRIlVLJkSd25c0effvqpAgMD9dtvvyl9+vT6888/JTHEHuYVEREhJycnlSxZUmfOnNGqVatUr149NW7cWOPGjVOOHDkkSbt27dKQIUOs73m83p695rm4uMhisejw4cMaMmSIJk2apGnTpqlt27ZydHSUJI0cOVKLFy/W7du3U6NkIFZPz/NQpUoV2dvb64MPPlCBAgWUNm1aPX78WBaLRX/++aeqVq2q77//XgMHDtQvv/yi/v37a9y4cdb3OfCyifyQ6ty5c+rRo4eqVKmi6tWra86cOdbh9L/88os++OADjR8/XuPHjyfMxyNtahcA4MmstGnTPvl1XL16tYKCgtSrVy/Z2dlp4MCBypYtm/7++2/lyJFD9erVU548eSRJ2bJlk62trTJmzCiJ+4lgDk/PTh8psne9bt266tWrl9atW6emTZtq8uTJypw5syTpypUrWrZsmTJnzqyCBQu+8Lrx8oic6fjZa17evHnVrVs3DR48WBEREZo/f76aNWsmJycnSdKhQ4e0Y8cO5cuXz/q+Al4WFotFN2/eVPv27VW6dGl999131ltEJEWZ6HbTpk1q3LixZs+erXnz5ilz5szauHGjdeQS8DJ5+kOqqlWryt3dXdmyZVPatGnVvXt31alTR4MHD1blypW1evVqNWnSROPHj1eaNGn08ccfK3v27Kn9El5KBHngJRAZ4hctWqSzZ88qc+bM6tChg3V5hw4dom1z6dIl/fzzzwoPD1fJkiVfaL1AUj0d4qdOnaobN26oYMGCqlu3rrJmzaoePXrI399f33//vbJnz65r164pc+bMOnHihFauXKkFCxZo0qRJevPNN1P5lSC1RL6HLly4oKVLl+rs2bNyd3dXvXr1VK5cOfXt21fnz5/X2rVr9fDhQ92/f1/Ozs5av369pk6dqrNnz2rOnDlKnz59ar8UIJorV67on3/+UefOna0h3s/PT8ePH9cvv/yiqlWrqlKlSipdurRWr16t3377TY8ePVL58uUJ8XipPP3/+8jnxHfv3l1vvvmmRo8erbfeekuSVKVKFfn6+iosLEyPHj2SnZ2dfv31VzVv3lz/+9//ZG9vr6+//prb6WJiAHgp/Pbbb4bFYjFcXFyM1q1bW5eHh4dHa7tz506jR48ehoODgzF27NgXWSaQIurWrWvY2NgYjo6OhsViMWrWrGls377dMAzDuHXrltGzZ0/DYrEY2bJlM8qWLWvkypXLyJgxozFmzBjrPiIiIlKpeqSWyOvhqVOnjBw5chi5c+c2ChUqZGTKlMnImTOn8eWXXxphYWHG/v37jcaNGxsWi8Vwd3c3cubMabi5uRl58+Y1jh07lsqvAojd5s2bDYvFYixYsMAIDQ015s2bZ1SoUMFwdHQ0cuTIYVgsFqNdu3bGf//9l9qlAjGaNWuWERgYGG25v7+/kTVrVmP69OnWZZ999plha2trzJ4927h3755hGIbx4MED6/oPP/zQOHXq1PMv2qQI8sBLIigoyJg6darh7OxsZM2a1di3b1+M7dasWWPY2toahQsXNqZOnWpdHlPgB1LLsyH78ePH1n+vWrXKKFCggPHzzz8bp06dMv744w8jQ4YMRvHixY0//vjD2m7x4sVGz549jXfffdcYOnSosX79eus63u+vr2vXrhnFihUz3n33XWP37t1GWFiY8eDBA8PFxcUoVKiQ4evraxiGYdy+fdtYsWKF0adPH6Nr167G7NmzjYsXL6Zy9cD/ie3DyAYNGhgWi8Ua3Bs1amSsWLHCMAzD+Prrr400adIYfn5+L7JUIEEWLVpkWCwWo3fv3kZERESU9/imTZsMBwcH4/z584Zh/L/27jouiu39A/hnlu5SQEoFBQVbMbC7sBvFVixMsFvsuIDYXgPr2n1tsbGxuQpYqIQBKM3uPr8//O18WbHu96vsos/79fJ1l9mZ4RzvcWaeOec8h2jMmDFiEK8I3qVSKR05coSysrJUUfwCh4fWM6YCivmduRkaGqJ79+6Qy+UYM2YMgoKCYGlpCUdHR6X9WrVqhc2bN6NYsWKoWrXqF8/HmColJibCysoKRARBEMRpIocOHcKFCxfg4uKCFi1awMjICK6urggPD4eHhwf8/f0hl8vRsmVLdO/eXfw3kbt9c3v/PSna0o0bN5CSkoJ58+ahVq1aAIDFixcjPT0d48ePR/HixQEApqam6NSpEzp06MDthakdxbDjDx8+QCqVIjk5Gfb29tDU1MTOnTuxcOFCvHr1Cu3atUPZsmXFOcIWFhYoXLiweE1lTF2sXLkSPj4+uHXrFry8vPIkqStbtiz09fWxefNmyGQyBAUFISQkBD169BCTNI4bNw779+/H+fPneV7891DxiwTGfju5eyafPXtGd+7coejoaHFbeno6LVy4kDQ0NKh3797im0siouzs7Dzn4+HFTN0cPHiQjIyM6Pjx40rb//jjDxIEgVxdXWnu3LlE9LFnXdGuIyMjyczMjMqUKaPU+85t/Pcll8vF0ReKHpuQkBAqVKgQJSUlERGRv7+/2KuTlpZGRESZmZl08+ZNbjtMLUmlUiIievDgAdWrV4+sra1JIpFQkyZNaP369eJ+n448unHjBjVq1Ihq165NycnJ+Vlkxr6qWbNmpKOjQzExMeK2hw8f0vDhw8Vh9klJSdSzZ08yNjYmTU1N2rBhg/hvgYjowoULVL9+ffLy8qLU1NR8r0NBxK+oGctHMplMfIvet29fNGjQAOXLl0flypXRvXt3PH36FHp6ehg+fDjmzZuHTZs2Yfr06Xj69CkA5Yy1Cpypnqmb1NRUmJiY4OHDh0rbfXx80L9/f0RGRmLbtm148uQJJBIJtLS0IJVKUapUKYSHhyMxMRGjRo3C/v37AXAb/x2lpqaKnyUSCe7du4cBAwYgISEBdnZ2SE5Oxrt37zB79mwEBgZi2bJl6NGjh5idvl+/fggKCkJWVpaqqsDYF2loaODRo0eoU6cOZDIZ+vXrh1WrVuHVq1fw8fHBwoULAUBpJMmmTZswbtw43Lp1CytXroSJiYmqis+YkjVr1uDKlSvYvHmzUi/6unXrsHLlSvj5+eHDhw8wNTXF4MGDYWVlBSMjI7x+/RoaGhogIpw8eRIzZ85EdHQ0pk2bBgMDAxXWqODgcTmM5SNF9s727dvj4sWL6NSpE8qVK4fTp0/j8OHDiIyMREhICDw8PDBkyBAIgoDJkydDKpVixowZcHJyUnENGPu2rl27olKlSnB2dgYA3L9/H25ubtDT00NQUBAEQcCaNWuwbt06DBs2TFyCRiqVwsXFBWfPnoWrqys+fPig4powVQgNDcX9+/fh7++PQoUKITExEZ6enihdujSeP3+OGjVqwM3NDdWqVcO7d++wefNmNG/eXByaef78eTx8+BB16tThIfVMLWVnZ2PWrFlwdHTE4sWLxezdFy9exMuXL2FraysuS5uRkYHZs2fjjz/+gKurK8LCwuDq6qriGjD2H7GxsQAAd3d36OvrIyYmBklJSZg5cyZSU1Oxb98+5OTkIDAwENWrV8fq1asxZswYTJgwAZs2bYJUKkV6ejpkMhkOHTokPjuw76DqIQGM/W4OHjxIxsbGtHz5cnHoUFJSEu3fv5/s7e2pSpUqFB8fT0RE79+/p/nz55MgCHmGKTOmjj4dCjpmzBjS0dGhs2fPitsyMjKoR48epKGhQdOnTxfbO9F/po8ohk2z38uGDRtIEAQaPXo0ERElJydTQkIC1alTR1zVgIho0aJFVKRIEXJ0dFSafnT+/Hlq2rQpOTo6Kg3xZEydpKSkUOnSpWnIkCHiNj8/P9LU1KQ1a9aIw+YVU0Xu3btHu3btolevXqmkvIx9jWKlhTlz5tA///xD+vr6NGHCBCL6eL/38fEhW1tb6tOnj5iZ/sGDB7R69Wpq3749dezYkebPn690LWffhwN5xvJZSEgICYJAd+/eJaL/zJXLzs6mXbt2kZ6eHg0cOFDcPzU1lW7cuKGSsjL2v9qyZQu5u7tT8eLF6cyZM+L2zMzMLwbzuTPdcnb638f69etJEAQaM2YMJSQkUGJiIpmbm5OLiwtVrVo1T46QKVOmkLm5ORkZGVHv3r2pRYsW5OLiQpaWlnTnzh0V1YKxvBT3+UePHlFqaiqlp6dTyZIlKSgoiIiIRo8enSd7NxHRtGnTxGcFxtSJIiAnIkpISKCJEyeSIAikr69PTZs2pfv374s5oT4N5j+3NB377/CYM8bymaGhIQAgJSUFwH/m/2ppaaF+/fqoUaMGwsLC8ObNGwCAgYEBKlWqBOBjtm7G1BXlyk4rk8kAAF5eXpgwYQLMzc3Rq1cvnD17FgCgo6ODtWvXolu3bpg3bx5CQkIQFxcH4OO/CcW/Cx4a/XvYuXMn+vbti5EjR8Lf3x+WlpZISkpClSpV8O7dO7x//x5JSUkAPg5LBoCZM2di/fr16NChA8LDw/HmzRs0adIEFy5cQNmyZVVZHcaUKObEe3h4YMaMGdDT00PRokWxceNGTJw4ESEhIQgODlbK3r1s2TL8+eefPMWIqZ0ePXqgXbt2eP36NQDA0tISHh4e0NbWRkZGBuzs7ODq6gpNTU1kZ2dDV1cXgYGB8PT0xPHjxzFy5EgxD4pUKgWg/PzA/gVVv0lg7Ff1aU+ioofx4sWLVKhQIWrRogW9fPmSiD5msld8P3LkSDI3N1fqoWRMHeXOCJ57NYacnBzKyMhQ2nfXrl1UuXJlKlq0aJ6e+a5du5IgCHT58uWfX2imdhTD6QVBoL59+yp9d/fuXercuTMJgkAjRowQt3+6xvCbN2+ISLkdMqZqip54uVxO3t7e1KBBA3Ga0eHDh8nKyooEQaC1a9cqHXft2jVq0qQJNWrUSGzbjKmDnJwcmjdvHhUqVEhpxSUfHx/y8PCgTp06kSAINH78eKVjiP7TM1+0aFHq3LkzZ6b/ATiQZ+wHUQz7lMlkSg+TsbGx9PTpU3r79q24bdiwYSQIAvn6+tKLFy/E7S9fvqSmTZtSnTp1eGkZptZ27NhBR48ezRM4DR48mDw8PKhq1ar0119/KQ2h+1Iwn5GRQUePHs23sjP1oRhO369fP/G6OGTIEKV2df/+ferYsSMJgkATJ04Ut+feR/FSiZebY+rmn3/+oR07dlDfvn1pyZIl4va3b9/SuHHjSF9fn+rXr0/Xrl2jV69e0ebNm6lBgwZUuHBhevDggQpLztjnZWVlUUJCAhERvXjxgjIzM4noY26bqKgoGjRoEAmCQGPHjhWPyR3Md+/enVxdXTnnww/AgTxjP8CGDRuoV69e9O7dO6XtPXv2pOLFi5OOjg6VKVOGpk+fLn7XpUsXEgSB6tatS4cPH6a9e/fSqFGjSFNTk1auXJnfVWDsuyUkJFCRIkXIwcGBTp06JW5v27YtmZiYUM2aNcnd3Z0EQSB/f396/PixuI8imHdyclJKXqbAc+J/H2vXrhUT2717947i4uJo7NixYjCfuy18KZjPvQYxY+omMzOTunXrRoIgkJ6eHm3evJmI/hPUvHnzhubMmUN2dnYkCAJpamqShYUFlSlThvM8MLWXlJRE1tbW1Lp1a0pMTBS3P3z4kHx8fL4azMfFxeV7eX9FHMgz9gO0aNGCNDQ0aMiQIWJPepcuXahQoULUq1cvmjJlClWrVo0EQaB27dqJGbn9/PzIwcFBHFZapEgRWrRokXhe7l1i6kgul9PFixfJ1dWVSpQoQSdOnKBr165R5cqVadu2bZSWlkYpKSk0Z84cEgSBfHx8lDKI79mzh8qVK0fGxsb04sULbue/GblcTunp6VS1alUaPny4OMWIiOj58+fk7+//zWB+ypQpqig6Y//ahQsXqHv37qShoaGUyFbRthW9m2vXrqWlS5fS8ePHeWodKxAyMjIoKCiIDA0NqVevXkrBeVRU1FeDefZjcCDP2A8glUqpS5cuZGhoSD4+PvT06VPy9PSk1atXi3OAnj59SvPnzyd9fX3q1KmTeOyjR4/o6NGjFBYWpvQGnnsmmTqTy+UUHh5OLi4uVK5cOZo/fz5VqVJFzGSrCM6DgoJIEAQaNGiQUjC/bds22rp1q0rKztRDSkrKZ+f/xsbGfjWYV+RUmDVrVn4Wl7Fv+tJLyYsXL1Lbtm1JEARavnz5N/dnTB3lfvmkkJ6eTqtXryZtbe0vBvMSiYSGDh2a7+X9HXAgz9j/SDG0UyqVUseOHcnY2Jjatm1LxsbGdOXKFSL6z8367du3NHv2bBIEQann/VN8c2cFQe5gXhAEKlOmjHiDz92GAwMDSRAEGjp0qFJyHAV+afX7+dY17mvB/O3bt6l37950//79n11Mxr6b4lkgJSWFXr16RQ8ePFDKjRMeHk6enp6fTW7HmLpTtO+YmBgaO3as0jNsVlbWV4N5Ly8vMjY2FufVsx+HA3nGfgDFBU4mk1GHDh3I2NiYzM3N6fr160SkPJQoOjqaSpYsSQ0bNqSMjAwO2lmB8umcZLlcTpcuXRKnjmzbtk1M/Ji7bQcHB5MgCOTt7a20/ixjX/K1YP7TrPWMqZLiuvjgwQOqVasWmZubkyAI5ObmRlOnThX3u3z5MgfzrMBRXHvv379PDg4O5O7unmd6U2Zm5heD+ZiYGJ4T/5MIRLxwH2M/Qk5ODrS0tCCXy+Ht7Y1t27ahbt262L17N8zNzZGdnQ1tbW0AQLdu3RAeHo5bt27B1NRUtQVn7DtJpVJoamoCALZv3w57e3vUqFEDRIQrV66gX79+yMnJwYoVK1CvXj1oamqCiMQ14efPnw9dXV2MGDFCldVgBciLFy8QHByMoKAgdO3aFevXr4dEIlF1sRgDAKXrW3R0NDw8PODs7Iz69evD2toa69evx82bN9G1a1ds3boVAHD58mXMnj0bR48exR9//IFhw4apsgqMfZenT5+iTp06KFeuHPz9/VG3bt08+2RkZGDTpk3w9fWFt7c3Zs6cCRsbGxWU9jei0tcIjBVgX8uWLJPJqFu3bqSpqUm9e/dWymYfGxtLHh4eVKtWLV5ijhUYudt7x44dydrampo1ayYmblQMs3d2dqaSJUvSiRMnxJEonxt1wiNR2Pd68eIFDRkyhMzMzLhXh6mFqKgopWuiVCqloUOHkoODA4WHh4vbHz9+TKNHjyZBEKh///7i9mvXrlHt2rXJ1NSUnwOY2pPL5TR37lyys7OjM2fOiPfvpKQkevjwIe3bt4/S0tKI6ONoKcWKJEOGDOGVRX4y7pFn7DudPXsWZcuWhbm5OeRyudgrNH36dMTHx8PQ0BCjR48W3z7K5XJ07doVu3btQtWqVTFz5kzEx8fj7t27CAwMRHBwMAYPHqzKKjH2r3Xt2hVhYWFYuHAhGjRoADs7O7FXiv6/Z75Xr14gIqxcuRJ16tQRe/EZ+2+9evUKmpqasLS0VHVR2G/uzz//xLJly7B69WpUqVIFwMcReQ0bNgQR4fz58wD+M4Lp5cuXmDRpErZu3YqtW7eiY8eOAIBbt27BwsIC9vb2KqsLY99r8ODB2LNnDxISEgAABw8exI4dO7B3715kZGSgfPny2L17N4oXL4709HTs2rUL7u7uKF26tIpL/mvjQJ6x73D79m1UrFgRXbp0wbJly2Bubg4AaNmyJcLCwmBiYoL09HTo6upi06ZNqFu3LnR0dJSG2RcqVAgSiQStWrVC2bJlMXz4cADKQ/MYU2cnTpyAt7c3/P39MWTIEOjp6eVpv5RrmH1aWhpWrFiBZs2acRv/jclkMmhoaKi6GIz9zzZs2IC+ffvC19cXU6dOhYWFBYCPL+5btWqFhw8f4tq1azAzM1O6NkZERKBq1aqYNGkSpk+frsIaMPZtuTurFD9v2bIFPj4+aNGiBfT19bF37144OjqiQ4cOsLS0xLRp01C/fn389ddfKiz574cnmjH2HWxsbDBjxgwcOHAAI0eORFJSEvbt24fo6GhxDtyhQ4dQrlw5dO7cGYcOHUJmZiYkEgk2bdoELy8vZGRkwMPDA3/88YcYxMvlcg5wWIHx+PFjJCYmokGDBtDT0wMAsf0q3gkLgoDq1atjzZo1yMjIQGJiIrfx35giiI+JicGMGTMwYsQInD17FsnJyaouGmP/iiKIHzVqFMaPHy8G8UQEiUSCsmXL4vHjx9izZw8yMzMhCAKkUikAoGLFirC2tkZMTIwqq8DYN8lkMkgkEjx//hwLFy5EfHw8JBIJGjVqhMGDB+POnTs4ffo0pk2bho0bN2Lq1Kno27cvXFxc8O7dO1UX/7fD4x0Z+w6FCxfG4MGDoaWlhalTp8LExASurq5wcnJCq1atoK+vDysrK6xduxbDhg1Dv3798Oeff6Jly5bQ1dVFaGgomjdvjsaNG8PQ0FA8LydtYgVJVlYWJBIJdHV1ASj3tCqC9adPn6JYsWKoUaMGbt++DWtra5WVl6mehoYGIiMjUadOHaSnpwMAli9fjsGDB2Po0KFwcXEBwCOTmHoLDQ0Vg3h/f3+l69rt27dRoUIFBAQEICwsDNOnT4epqSmaNGkCIyMjAMC5c+eQlZWFMmXKqKoKjH2T4p7+4MEDtG3bFikpKYiPj8fixYtRpEgRzJkzB7NmzYJMJhPbNgDcuXMHHz58QOXKlcUOKr6e5xMVzMtnrMB6/fo1zZkzh3R0dMjAwIB69OiRZ5+nT5+Sp6cnmZiY0J49eygjIyPPPpzoixUkiqVnzpw5Q0ZGRtS7d2+xXedeWvHs2bNUvXp1unPnzmePZ78Pxf/zrKwsGjRoEDVr1ozOnTtHz58/pzlz5pAgCNS5c2e6e/eueAxfF5k6ioyMJC0tLSpevDg9fPhQ6bvVq1eTIAgUFhZGRES3bt2i8uXLk7m5Ofn6+tLVq1dp3bp11KhRI7K2tqaYmBgV1ICx7xcVFUU2NjbUpEkTsV3n9ul1+sSJE9SsWTMqUqQIRUVF5VMpmQIH8ox9w6cZN1+9ekULFy4kc3NzKlmy5GcvXM+ePaM2bdqQrq4ubd26Vekc/LDK1NnXMsxmZWVRq1atSFdXl+bMmUOZmZnid3FxcTRlyhQqVqwYXb16NT+KytTckydPaOfOneTh4UGBgYFK361Zs4YEQaAuXbpwMM/UWmxsLE2ePJn09PRoyJAhlJKSQkREf/75J2lpadH48eOVMs8/f/6cWrZsSRKJhARBIAMDA3JxccnzgpMxdSOVSqlfv37k5uZGV65cEbfn5OTQ27dv6fXr15SdnU1ERCkpKTR06FAqWbIkFStWjG7fvq2qYv/WeGg9Y9+gGDp89OhRNGvWDEWKFIGXlxfkcjmmTp2KGTNmICgoSEyABwAODg4ICgpCr169kJycrJToiYcbMXWVe6j8vHnzcO/ePbx+/RoDBw5E1apVYW9vj61bt6JWrVqYMWMGbty4gREjRuDly5c4ffo01q9fjwULFsDd3V3FNWGqJpVK0bVrV1y9ehUlSpRAixYtAADZ2dnQ1tZG//79QUTw8fEBEWHq1Klwc3Pj6yNTO3Z2dhg2bBgkEglmz54NLS0tODs7Y9iwYZg0aRL8/f1hbGws7m9vb49Dhw7hypUriI+PR6FChVCiRAlYWVmpsBaMfZtMJkNkZCQcHR1RtWpVAMDJkydx4MABbN68GRoaGujVqxfGjBmDnJwcfPjwAa1atcLQoUPh6Oio4tL/plT9JoExdZW7Z3L8+PFkYWFBS5cuFbfFxcXR3LlzSUtLi3r27Km0VrzChw8f8qWsjP2vcg9/b9myJZmZmZGbmxuVK1eOdHR0qF+/fnTr1i0iIkpNTaWuXbtS4cKFSRAEEgSBihUrRn/88Yd4Du5ZZQ8fPqRq1aqRIAg0cuRIcZ3h3NMxFD3zzZs3pwcPHqiqqIx9U3x8PE2dOpV0dHRIEAQaO3YsvX//Ps9+vG42K8h69epFhQsXptWrV5Ovry8VLlyYHB0dycfHh/r27UuCIND27duJ6OMzbu6ReSz/cY88Y5+Ru2fy+vXr4jJby5cvh5aWFnx8fGBtbY0+ffoAAKZOnQoACAwMhJmZmXgeRWI74kROTM0pEi8OGjQId+7cQUhICBo1agRLS0t4e3tjx44dSEtLw4QJE1CuXDls3LgRMTExuH//PiwtLWFhYQE3NzcAeZeuYb++z13jnJ2dsXXrVrRv3x5bt25FlSpV0LFjR+jo6IhrbPfv3x8ZGRmYOHGiUq8mY+rGysoKgwYNgo6ODgICAvD69evP3td5qUVWEHzpuXTEiBGIjY3F0KFDYWRkBB8fH7Ru3RrVq1eHVCrFwYMHcebMGXTu3FkpeTNTDQ7kGfsEEYk34jZt2uD27dsoWbIkKlWqhEuXLmHq1KkgIgwaNAhWVlZiMD9z5kxIpVIEBweLy9IocBDPCoKrV68iLCwMw4YNg6enJ4yNjXHhwgXs378f9vb22LNnD+RyOaZMmYIyZcqgdOnSKF26tNI56P+XYmK/D8WLz9TUVLx//x6ZmZmwsbGBrq4uHB0dsWfPHnh6emLcuHEQBAEdOnSAjo6OeJyvry969uwJExMTVVeFsa8qUqQI+vTpg+zsbMyaNQs6OjqYN28et11WoCiuvW/fvsWjR48gl8thbW0NJycnVKhQAQcPHsSNGzfg6OgIa2tr8Zn47Nmz0NHR4dUX1IkKRwMwptamTJlCurq6FBoaSqmpqUREdPfuXXJyciIHBwdasWKFuG98fDzNmjWLBEGgY8eOqarIjP1P7t+/T926dRMzM9++fVtcnUEqldLw4cNJEATq3r07J7ZhRPSfYcSRkZHUtGlTcnBwIAsLCypfvjzt37+f3rx5Q0REMTExVLp0abK1taWtW7eKwzEVx/NUDFaQKIbZa2ho0KBBg8QEeIypO8U19/79+1S+fHkyMTEhHR0dsre3p9DQ0C8eFx4eTi1btiQnJyd6+vRpfhWXfYNARKTqlwmMqcrGjRvx9u1bjB49Os93rVu3xrNnz3D9+nVoaWkhJycHWlpaePz4MRo2bIj09HTMmDEDgwYNAgDExcXh5cuXqFKlSn5Xg7F/jT4zrC4nJwdJSUmwtLREQkICGjVqBDs7O4SEhMDJyQn37t1DvXr1oKGhAXd3d4SGhioleWS/vs+1m6ioKHh4eMDe3h516tRBdnY2zp07h8ePH2Ps2LHw8fFBkSJF8PjxY7Rq1Qrp6emYOnUqunfvDm1tbRXVhLH/TUJCApYvX47Zs2dj0KBBmD17NvfMM7WT+5qt+BwVFYU6derAyckJXl5eMDMzw969e7Fr1y6sWrUKAwYMEI/Pzs7GmDFjcOnSJbx69QrHjx9H2bJlVVUd9gkeWs9+W6mpqThy5Ah27NiBevXqoVKlSgA+XuikUimys7PF/2ppaUFLSwsymQyOjo7Ytm0b6tWrh2XLlkEqlWLYsGEoUqQIihQpAoDnCDP1ppifDABZWVnQ0dEBAGhpaaFw4cIAgMePHyMhIQETJ06Ek5MTACA2NhYODg6oVq0aypUrx0H8b0gQBPH6prhWzpgxA1ZWVli9erX4IjM2NhaTJ09GQEAAjI2NMWLECDg6OuLQoUOoWbMmFi1ahA4dOnAgz9TG515SfY2VlRWGDBkCDQ0NTJ8+HTo6Oli0aBFPpWNqIzo6GiVKlBCH0guCgPfv32Ps2LEoXbo05s6di2rVqgEAzp8/DwDw8fFBRkYGhg8fLm5//PgxnJ2dsW3bNjg7O6usPiwvDuTZb8vQ0BCzZs2Ct7e3GMQDHx9UtbS0UK1aNRw/fhzh4eFo1KgRAIgPr25ubnByckJiYiJCQkJgaWmJzp07i+fgIJ6pK7lcLgbxw4YNwz///ANjY2MMGTIEVatWFROOJSUlITk5GampqQCAxMREnDhxAra2tlixYoV4vn/78MsKpiFDhiAtLQ0bN24Ur2+Ka+WdO3dgZ2enNBrJ3t4eS5cuRXp6OgICAtC+fXsUK1YMxYsXx6VLlyCTyTi5HVMbikAnKSkJ2dnZICJYW1t/8zgrKysMGDAA2traaNOmDV8LmdoYN24cFi5ciCtXrsDd3V1s469evcKjR48wZMgQMYifOHEi1qxZgzlz5uDu3bsYOXIk9PT0MGDAANSrVw+urq4wMjLi5HbqSDUj+hlTP23btqWFCxeKP798+ZLKly9PxYoVo/v37yvt++jRI6pXrx7t2rWLHBwcqGbNmpSYmJjfRWbsv9ahQwcyMDCgcuXKkY2NDRkbG9OsWbPEdhwXF0dOTk7k5OREPXv2pC5dupC2tjYFBQWpuOQsvyUnJ1PVqlWpVKlS9Pz5c3F7Tk4OZWdnk7OzMzVr1kzcrljKUCaT0bFjx0gQBJo1a5bSd4ypC0V+hvv371Pp0qWpcOHCZGdnR8uWLRPz43wLLznH1M3ff/9N1apVIwMDA7p69arSd2vWrBHbbEhICGlqatKKFSsoJyeHTp8+TYIgkEQioblz56qi6Oxf4ECeMSJKSEigDh06kIaGhpjETiqV0vbt28ne3p6KFStGu3fvpsTERIqJiaFJkyaRvb09JScn0+HDh0kQhK8mCWFMnTx//pxq1KhBe/bsodevX1NSUhK1bNmSBEEgf39/evXqFRERRUVFUZ06dcje3p7c3Nxo6dKl4jk4OdnvQfH/OSEhgR49eiR+zm38+PEkkUhoy5Yt4rasrCwiIsrMzCRDQ0MaN25cPpWYsX8vMTGRnJ2dqXr16tSvXz9q2rQpCYJAvr6+Si+vGFN3uV+WHj9+nKpWrUp6enp5gnkiohcvXlCFChWoX79+YmJSIqJKlSpR1apVydzcnN6+fZsv5Wb/HR5azxgAS0tLLFiwAObm5hgyZAhkMhmGDh2Kdu3aQVNTE/Pnz0fHjh1RuHBhCIKAxMREzJkzByYmJnB2doaOjg7++ecfVVeDsc/KPSceAN6/f49nz57B3d0dhQoVAgAcOnQI7du3x6JFiwAAI0eORIkSJXDgwAHk5OQgOzsbNjY2ADgHxO9EMSfe0tISlpaWePHiBZo3b466desiJCQEANCyZUvs3LkTs2bNgr6+Ptq2bSvOfT9//jx0dHRgb28PgKdiMPWiaI/Pnj2Drq4uAgMDUa1aNSQkJGDVqlWYMWMGsrKyMHnyZLENM6bOJBIJkpOTYWpqisaNG0MikWDixImoW7cuzp49qzTMPi0tDffv38eAAQNgYWEBIsKBAweQkZGBlStXws3NjXPhqDkO5NlvR3EB+5SjoyP8/f0hl8vh6+sLIsKwYcPQtm1bNG7cGH/++ScePHgAXV1d1KxZE126dAEA3Lp1C9ra2nBwcMjvqjD2TTKZTAziFyxYgCdPnsDS0hKVKlWCnZ0dACAzMxO6urrYs2ePGMxraGhg+PDhYgJHBeJ14n87uf9/a2trw8bGBocPH4a+vj4WLFiAWrVqISAgAKNGjYKPjw8iIyPRsmVLREZGYt26ddDW1oanpycAcBDP1ILiOSAzM1NM9unq6irOGbayssKYMWOgqamJKVOmAAAmTZrE93mm1s6cOYPdu3dj06ZNmDhxIsaOHYuGDRti7ty5mDBhglIwT0SwtbWFo6Mjtm/fjg4dOiAiIgLr1q2DiYkJypUrB1NTU1VXiX2LKocDMJbfcnJyxM/btm2jWbNmUWBgIB08eFDcHhUVRf369SNBECgkJOSr57ty5Qo1bNiQihYtSs+ePftp5Wbsf9W6dWuSSCRkbm5OgiCQIAhKOSEU63oTEXXs2JEEQaChQ4dSenq6KorL1IRimKZcLheH2cfFxVHHjh3Jzs6O/Pz8xH337dtH9erVIy0tLRIEgQwNDalEiRJ0+/ZtlZSdsc9RtOnIyEiqX78+lSlThooVK0b29vb08uVLpX0/fPhAs2fPJolEQoMHD6YnT56ooMSMfVtoaCgVLlyYqlevTqNGjaINGzaIU5yIiE6dOpVnmH1WVhatXr2aihQpQoIgkIGBAVlZWfE1uwDhQJ79NnLPG/L09CQjIyMyMzMjfX19MjQ0pAEDBojfx8TEiMH8ypUrxe2KB9mcnByaNGkSlSlThqysrOjWrVv5VxHGvkPu5Ev37t2j0qVL09atW+nly5d06dIlKlKkCNna2tKqVavE/XIH840bN6Y//vgjP4vM1IyiDT19+pSWLFlC165dE9vIl4L5x48fU3h4OAUGBtLhw4fzBEaMqYPY2FiytLQkNzc3ql+/PlWtWpUEQaC5c+dSUlKS0r6pqak0f/58EgSBRowYodQhwJg62L17N+no6JCvr2+eIDz3s0DuYP7y5ctE9PFl1d27d2n+/Pm0atUqfllVwHAgz347AwcOpCJFitDGjRspJSWF3rx5Q23atCFBEOivv/4S93v8+DENHDiQBEGgJUuWKJ3j2bNnNH36dOrWrRtFRkbmdxUY+27+/v7k7+9PjRo1ovfv34vbr1+/TiVLliR7e3ull1W5g3n2+1K8+Lx//z6VKFGC7O3tafHixUrf5Q7m/f39VVZWxr6HIqBJT0+nI0eOUKNGjejOnTuUmZlJUVFR1KRJE9LX16fAwMA8wfyHDx9oyZIl9ODBAxWUnLEve/r0KVWtWpXat29Pjx8/Frfn7rzKnZz2c8E8K7g4kGe/ldevX1PZsmVp3Lhx4o06PDycDAwMqH///nneRD569Ii6du1KgYGBec6Vnp7Ow46ZWjt48CBpa2uTra0tderUiYg+DqVTPNBev36dSpQoQQ4ODrR69WrxuC89ALDfS3R0NBUpUoSaNWtGJ0+eVPpO0S5evXolBvMTJkxQRTEZ+24PHz6kunXrUsuWLalVq1ZK3yUnJ1PTpk3JwMDgs8E8XwuZOjp//jzp6urS+vXrv7pf7vu6Ipg3Njamixcv/uQSsp+JA3n2W7l9+zYJgkBHjx4loo8XM319ffLy8lIaAnrp0iXxc0pKSr6Xk7EfZfHixWRlZUVaWlp0/fp1IvrYM5U7mC9VqhRZW1t/9oUV+/Xt3r1bXHJQQSqVko+PD5UrV05p2aLs7GyKi4uj2NhYevfuHRERvXz5krp06UJ6eno0ffr0fC07Y//GtWvXxDWyu3TpIm5XXA+Tk5OpSZMmZGBgQMHBwWIbZ0xdLViwgLS1tSk2NpaIlIfSf82JEyeoZMmSVKRIEcrIyOAXVQUUpx5mvxVTU1NYWVkhISEBp06dgqenJ9q1a4dFixaJS2vt3bsXvr6+iIyMBAAYGxsD+Jitm7GCQiaTAQBGjx6NyZMnQ19fH7169UJERIS4aoNMJkPlypWxadMmSKVSGBkZqbLITAU2btyIjh07im1AQUNDA0+fPoWpqSnKlSsHADh27BiGDx8OR0dHVKpUCYMGDcKTJ09gY2ODJUuWoEuXLvDy8lJVVRj7LMW9WyaToUqVKrh27Rr09PSwY8cObNu2DcDH9i6TyWBiYoIdO3agbt26GDFiBHbs2MH3fqbWLCwskJOTg5s3bwLAZ1dlAj7+O3j79i0OHToEAGjUqBHWrl2LixcvQldXl1cUKaAE4isU+wV9aYk5AKhXrx6ioqKQnJyMdu3aYfHixbCysgIAPHv2DP7+/khOTsaGDRvE4J6xgij3eu+BgYFYsGABrKyssH79elSoUEEM9jU0NJCYmAhLS0tVFpepyPjx49G2bVtUr15dqc14eXnh+PHjGD16NF6+fImdO3fCzMwMbdu2xdu3b7F582YsWbIEAwcOhKam5levu4zlt9xt+VNXr15F3bp1UbRoUcyfPx9t2rQB8J9nh+TkZPTv3x8BAQEoVapUfhabsX/l/PnzqFu3LkaOHIklS5aAiPIE5YptKSkpMDMzw65du9C+fXsVlZj9SBzIs19O7ofJ06dPIycnB+XKlRPXw7558yb69OmDqKgo7N69G/Xr14euri4eP36M0NBQBAYGIjg4GD179lRlNRj7LlKpFJqamuJ/P/WlYH7Dhg0oX768+O9FcaP/2sMv+7U9ePAAR44cQbdu3WBjY4O0tDQ0aNAAkZGREAQBI0aMQIsWLVC9enUAH9fa7tChA5YvX67ikjOmTHFde/nyJY4dO4bExEQYGxuja9eu0NfXh66uLi5duoSGDRuiWLFimDdvXp5g/nMBEWPqJjs7G15eXjh06BAOHDiAJk2afPE+vnz5csyaNQsXLlyAk5OTCkrLfjjVjOhn7OfIPcenTZs2pKmpSYIgUKFChWjdunWUkpJCMpmMdu7cSUWLFiVbW1vq1q0b+fn5Ua1atUhPT4/mzp372fMxpm5yt89x48bR4cOHP7tf7iQ3S5YsIXt7e3J1dRXnzDNGRDRhwgQSBIFmz55NcXFx4vYbN24o/SyXy+ns2bNUtGjRPCt6MKZquVdcKFasGJmbm5O2tjYJgkClSpWi5cuXi4nsLl68SLq6ulSqVCnav3+/CkvN2Nd97Xl0z549ZGlpSbq6unT+/PnP7nPr1i1q2LAhtWjRgpKTk39WMVk+40Ce/TJyByvTpk0jW1tbWrx4MW3cuJE6d+5MmpqaNG3aNPEGfvfuXWrbti05OTmRhYUFtW3blkJDQz97PsbUTe722b9/fzI1NaUDBw5QVlbWN/dfuHAh6erq0pYtW356OZn6+tw1bvTo0SSRSGjmzJl5EuApnD9/njw9Palo0aJKyx0xpiqftuWXL1+Sk5MT1a9fnw4ePEiPHz+mvXv3UuXKlUlfX5/mz58vJrINDw8nIyMjsrKy+uLLUMZU5Z9//hE/fxrM5/45JCSEjIyMSEdHh1atWkUxMTHid4cPH6ZmzZqRubk5L6H4i+FAnv1ynjx5Ql27dqWxY8eKQU1mZib5+fmRIAg0ZcoUSkxMFPdXrCWfk5MjbuMgnqmz3DfvmJgY8vb2piVLllBGRsZXj8vdrm/evPnTysfUnyKzcXx8PF24cIFSU1PF70aMGEESiYRmzZqldK3Mysqinj17UtWqVcnGxobu3LmT7+VmLLfcgXfu69u2bdvIzMyM9u7dq7T/hw8fqFq1amRhYUHHjx8Xt58/f56sra0pOjr6p5eZse/Vq1cvqlKlitJKSp8G87nb/YYNG6hSpUokCAIVLVqUWrRoQe7u7lS0aFEqWbIk3b59O9/KzvIHT4Rkv5TevXujdu3aePToETw9PaGtrQ25XA4dHR0sXLgQfn5+CAgIQEhICF69egXgY1Z6CwsLcX4xEfEcYabWFPM2e/TogQ4dOuDgwYOoVKkSdHV1IZfLv3icRCIRE9xVrFgRAL66P/s1KeYAR0ZGokWLFujatSv+/vtv8fvAwED4+vpi2rRpWLVqFV6/fg0AeP78OWJjY+Hk5IQzZ86gbNmyqqoCY5g3bx48PT2xdOlSAMrXt8ePH+P9+/fiPGC5XA65XA5DQ0Ps3r0bWlpaWLZsGYCP9/xatWrhyZMnPG+YqRV3d3c8e/YMs2bNwqVLlwB8vP9TrvRmEolEvI/36tUL27Ztw9KlS+Hg4IDY2FiYmprC19cXJ0+eFFcgYb+OvJmRGCugiAitWrXC+fPnERERgZMnT6JatWrQ1tYWk9YsWLAAwMcHAJlMhmHDhsHa2lrpPJzchhUURkZGePz4MTIzM/Hhw4fvOubTrOL80ur3o6GhgejoaNStWxeVK1fG6NGj0alTJ6V9AgMDAQDTpk0DAAwePBglSpTA/v37IZFIYGBgkN/FZkxJ/fr10aZNG8ycORMA4OvrK17fXFxcIJfLERkZKb5wkkgkkEqlsLW1RePGjXHy5Ek8e/YM9vb2EAQBOjo6KqsLY7kpnlmHDh0KAwMD+Pv7Y+bMmZg6dSo8PDzEYF7xvCqRSMSfnZ2d4ezsjEGDBvEqIr8D1Q0GYOzHy87OpiNHjlDRokWpbNmyFB4eLg5Dyj0cadSoUSQIAoWHh6uqqIz917Kzs8XP06ZNI0EQyM3Nje7fv09EnKSRfZlcLqesrCzq0aMHlS1bli5fvix+pxhun9uIESNIR0eHxo8fT69fv87PojL2TTdu3KDWrVuTubk5BQYGittjYmKocuXKZG1tLQ4nzp0/pG/fvlSiRAlO+sXUlkwmE+/loaGhVKhQIWrZsqVSMruv3etzD7nnZ4JfF3fFsF+KlpYWGjVqhGXLliEhIQGjRo3C9evXxTeV9P/DkZYsWYJz586Jyygxps4Uw0UVtLS0xM/Tp0/HhAkTEBUVhXHjxuHhw4d5ht4xpiAIArS0tHD16lWUKlUK1apVE79T9N7kbjuBgYHo0aMH1qxZk+9lZexLiAgymQyVKlXC9OnTUb9+fQQEBIhLITo6OmLAgAEgInTt2hURERHQ1tYGANy4cQM3b95E6dKlP7tkJ2PqQPHc+vLlS1StWhWlS5fGlStXEBAQgCtXrgDIO8w+t9yj7Xik6a+L15FnBc73rHMtk8lw9OhR9O3bF46OjggODkaVKlXyDEf63vMxpiqK+cwAEBISggcPHiA+Ph7t27eHu7s7XFxcAABjx47FsmXL0LBhQyxcuBAuLi68DjLLg4jw7t07lChRAj179kRQUBCkUqlSjhBFm0lNTYWhoSEAICEhAVZWViorN2O5Ka6LMTExuHLlCtauXYt79+7h7du3WLZsGQYNGgQAWLRoEQIDA5GYmIguXbogJycHUVFRePLkCS5cuABXV1cV14SxvBTX4cjISDRq1Aj29vbQ0NCAVCrFtWvX0LBhQ8yaNUvsjOJ7/e+LA3lWICh6JHPP93n16hVsbGy+eowimHd2dsaiRYtQtWpVvtixAiP3zblVq1a4dOkSDAwMQER4+fIlatasiVGjRqF9+/YAgHHjxiEkJARNmjTBnDlzULp0aVUWn6kh+rhaDdq2bYubN2/i6NGjKFOmDADll0Z+fn6wtbXFqFGjVFlcxr7o4cOHqFWrFtzc3GBhYYFixYph9erVICIEBARg5MiRAIDDhw/jwIED2LdvH4yNjeHm5oa5c+fy9ZGptaSkJDRp0gQAEBQUBA8PD+Tk5GD58uUYP348ateujVmzZomjqjiY/03l3yh+xv472dnZtGHDBlq2bJm4rXXr1uTr60tpaWlfPVYqldLhw4fJxMSEXF1dKT4+/mcXl7Efzt/fn8zMzCg0NJTevHlDb9++pQ0bNpC+vj65urrSvn37xH0nTpxIGhoaVK9ePUpKSlJdoZnKKea8S6VSys7OVlpic/Xq1SQIAnXp0oUiIyOVjouIiKCqVatS//79KTMzM1/LzNj3yMzMpNatW5OTkxNdv35d3H7u3Dlq1KgRmZqaUnBwsNIx8fHxlJ2d/c3nBsbUwaNHj8jCwoImTpwoblPMdV+1ahUJgkANGzb86tJ07NfHk4OY2tPU1ERYWBhCQ0Px7t07hIWFISIiAgMGDPhmllkNDQ00bdoUGzZsQGJiIg8NZQVOeno6Ll68iBo1aqBr167Q0tKCXC5Hr169YG5ujs6dO2PZsmWoVKkS7O3tMXv2bKSmpsLZ2RmmpqaqLj5TEUXvelRUFJYsWYK7d+/C3NwcFSpUwMyZMzFgwAA8fPgQS5YsQWJiIvz8/FCxYkWcO3cOq1evxvPnz7F582bO5M3UUlZWFqKjo1G+fHlUrlwZwMdpcopeysGDB2PMmDHQ1dXFgAEDAEBcZjZ3jhHG1FVOTo74B/jPaCpBEDBw4ED8888/CAwMhKGhIYYPH44GDRpwj/xviIfWM7VGuYYK1a1bF9euXYOGhgb+/PNPdO7c+X8+J2Oq9ml7zD1fGQBev36NypUro3r16tixYwdkMhkkEol4zPLlyzFs2DAcOnQILVq0+Ob52a+Pcs2vrF27NiwsLODo6Ii0tDRcu3YN1apVw44dO2BpaYnp06dj9erViI+Ph5aWFnR0dGBtbY3du3fzOvFMbWVlZaFu3boAgPPnz4svOBX5bnbu3IkuXbrAzMwMY8eOxbhx41RZXMa+6dN7dWZmJtzd3aGlpYUTJ07AwsICwMcAX0tLC3v37kX37t0hl8vh6emJTZs2QU9PT1XFZyrCGb6YWhMEAdnZ2eLPmZmZkMlkuHv3rviWUi6X/+tzMqYuBEFAVlYWHj9+DABiEL9p0yYAQOHCheHs7IwbN24gLS0NGhoakMvlYrtv0qQJ9PX1cerUKQB5M9xze//9CIKAlJQU9O/fHxUqVMCmTZtw5MgRhIWFwcPDA+fOncOdO3cAfFz1YM+ePdi8eTPGjRuH9evXIywsjIN4pjY+7W+SyWTQ0dFB2bJlcevWLRw4cABSqRQSiUR8XqhTpw7s7Ozg4OCANWvW4N27d7ySB1NLinv2p8+yurq6mDZtGu7fv48xY8YgJycHRCSOKElMTISnpyfWrFmD2bNncxD/m+JAnqktxcVNsWTMxIkT8ddff6FixYpYsmQJZs+ejbS0NEgkkjzBC2MFyZkzZ9CpUyfs2rULAFCvXj34+PjgwYMHAIBhw4bhxYsX4hBRDQ0Nsefpw4cP0NLSQtGiRcXvGEtJScHjx4/RqVMnuLu7AwAmT56MS5cuYe3atahataq4b/Xq1eHl5YWZM2eiQ4cOsLW1VVWxGVMik8kgCAI+fPiA169fQyqVite4uXPnwsbGBtOmTUNYWBiysrLE54Vr167B2toac+bMwdmzZ2Fubs4vNZnaUUyBio6OxuDBg1GnTh3UrVsXa9euxYsXL9C2bVuMGjUKoaGh8Pb2xqVLl5CVlYWzZ89i586dkEql8Pb2FlevYb8fniPP1FLu4cW7du1ChQoV0LRpUwBA586dUbt2bSxcuBDAx+zKiiWSMjIykJWVxXODWYFibW0NCwsLdO3aFW5ubnj79i22bNkCJycnAEDNmjXh4+ODkJAQEBH++OMPWFtb48WLF9ixYweIiJdR+g19unRm7qGZL168QEJCAmrXrg1BEODv74/AwEAsX74cXbt2hb6+PtLS0rBlyxYMHDhQVVVg7Ivkcjk0NDQQGRkJb29vvHr1CsbGxpg4cSKaNGkCa2trrF+/Ht7e3hg4cCD69OkDb29v3LhxA2vWrAHwsWfewMBAxTVhLC8iEtt3nTp1YG1tDRsbG0ilUgwZMgR16tTBypUr4efnBxMTEyxYsAA7duxAoUKFkJOTAw0NDZw5c0bV1WAqxnPkmdrJvQRSt27dcOLECTRo0ACbNm2CRCKBlpYWsrOz0bBhQ0RERMDPzw+TJ0+GTCbDqlWrcPPmTfzxxx8wMzNTcU0Y+363bt1CnTp1kJaWhlGjRmHRokUA/hOsvXjxAosXL8bq1athYGAAR0dHZGZm4sGDB5g1axbPAf1NSaVSZGVlQRAE6Ovri9ujoqJQt25djB07FrGxsQgODsayZcvg7e0tDsEMCAjA1q1bceTIEXFEB2Pq5OXLl6hcuTLs7e3h6uqKuLg4nD59GmPGjIGvry/s7Oxw9+5d9OnTBzdv3gTwcVRS4cKFcfToUZQrV07FNWDsy5KSkuDp6QkdHR3Mnz9fHD1VuXJlxMfHY8OGDWjcuDGys7Px4sULrFixAmlpaTA1NUXfvn1RokQJFdeAqRr3yDO1owji27VrhytXrmDevHlo1KiRmD05JycH2traOHXqFBo2bIjFixfjxo0bsLGxwZo1azB//nwO4lmBkLtH9cmTJ3B0dAQRISgoCFWrVkXnzp0hCAKICHZ2dpg6dSratGmDFStWIDY2Fm5ubhg3bhy6deuW53zs1xYREYG///4bmzdvRlJSEvT19dGnTx80adIE1apVQ8mSJVGpUiWMHj0ampqaCAoKQv/+/cX2ER4ejr///huVK1dGoUKFVFwbxv5D8TI/MzMTV69ehbOzM5YuXYry5csDAPz9/bFw4UJkZGRgzJgxKFu2LK5cuYITJ04gNjYWJiYmqFGjBuzt7VVcE8a+7uXLl4iJiUFAQIAYxE+cOBF37tzBypUrlaZAOTo6iiNROYktE+XnWneMfa8VK1aQlZUVbd26lT58+EBE/1kf88OHD5SRkUFERFlZWdSxY0eyt7cnGxsbWrx4sXgOXk+TqTPFGt9ERHFxcUREFBsbSxcuXKDmzZuTpqYmbdmyhYiIZDJZnuNlMpnS9s/tw35NO3bsoNKlS5ODgwN5enpShw4dqGLFiiQIAjk7O9OmTZuIiOj169dUq1Yt0tfXp9WrV1NycjIREZ04cYKaNm1Ktra29OjRI1VWhbHPioqKombNmlGXLl2obdu2eb4fP348CYJAw4cPp+joaBWUkLF/T3HfV9yvT5w4QUZGRhQZGUlERH5+fqSlpUWrV6+m9PR0cd+DBw9Samqq+FzLz7dMgQN5ppbGjh1LRYsWpSdPnhAR0cuXL2n58uXk4eFBDg4ONGzYMLp79y4RfbwwPnv2jGJiYsTjOahh6ix3ED9s2DAqU6YMLV++XNx2+fJlatq0qVIwT0SUlJREZ8+e5Zv5b2zDhg2ko6NDffv2pVOnTonbk5OTKSQkhCQSCRkYGND69euJiOjOnTvk7u5OgiBQ0aJFydXVlYoUKUIODg50+/ZtFdWCsa+7cuUKSSQSEgSBWrZsKW7Pzs4WPyuC+VGjRind/xlTR4r79b1792jt2rVE9PGFla6uLoWEhNC8efNIU1OTVq1aJQbxREQjRowgd3d3SkhIUEm5mXrjofVMLWlpaeHNmzc4f/48rl69ijlz5uDdu3ews7NDtWrVsGzZMhQpUgSlS5eGhoYGHBwcxGOJiIcXM7VF/5/gBgBatWqFW7duoUePHuKayABQrVo1zJo1C4IgoFevXgCAihUr4sCBA5gwYQLOnj0rJjFjv4/Q0FD06dMHo0aNwvDhw5XmtZuYmGDo0KGwsrJCz549MW7cONja2qJx48a4fPkyFixYgKioKKSkpMDDwwPt27dHsWLFVFcZxr6iatWqCA8PR+fOnXHy5Ens2bMH7dq1g5aWlriO9ty5c6GpqYnZs2dDW1sbAQEBYpJcxlTt3r17KFWqlNgmiQiZmZno0qULmjVrBgBwcHBAt27d4Ofnh6ysLGzduhWtWrUS85iEh4fj1q1bKFWqFCdtZJ+n4hcJ7DeXu2cyt6SkJKpbty4JgkCCIFDjxo3FHiYiIg8PD6pevTplZWXlU0kZ+7FmzZpFhQoVoq1bt1JaWhoRfRxJkruX/dq1a9S6dWsSBIHs7OxIW1ubpk+frqoiMxU6ceIECYJAPXr0oPj4eHH750ZnhIaGkiAI1LFjR3r37l2+l5Wxf+NrI+jCw8PJ0tKSypYtS0eOHBHbee6e+ZkzZ9L9+/d/ejkZ+15DhgwhMzMzOn36dJ7n3BIlStCMGTPEn0+dOkUVK1YkY2NjWrdunbg99xSohw8f5lvZWcHCWeuZyuReYi4sLAxxcXEoVKgQSpQoAUdHRwDAnj17YGZmhho1akBXVxfAx6Rg3bp1Q5kyZbBq1SpeN5sVSC1btsTbt29x5MiRPMkZP02Cd+jQIdy/fx/16tVD165d8+zDfm1yuRy7d++Gv78/DA0NsWvXLpQqVSrPfpQrAdKAAQOwdetW3Lx5k9cYZmpLkdguMTERDx8+xLt37+Dq6oqSJUuK+4SHh6NNmzYoUqQI5s+fL/ZmKnrmGVM3YWFh8PHxgZaWFkJCQlCnTh1oaGiAiODk5IThw4dj5MiR4v4HDx5EQEAArl27hvLlyyMrKwvp6emQyWQ4fPgwr77AvogDeaYSuYOQjh07IiwsDMnJySAiVK5cGT179oSvr2+e454/f46NGzdiyZIlWLFihRjUMFaQpKamws3NDTVr1sTWrVu/65jcL744iP/9ZGZm4tixY/D19YWpqSm2bNmCsmXLfnH/tWvXYuDAgdizZw/atm2bfwVl7DsprmMPHjxA+/btERsbi4yMDDg6OqJ169ZYsmSJuG/uYH7hwoVo3LgxTy1iaouIEB4ejp49e0JLSwvLly9H9erVoaenB3t7e0yYMAFDhgxRuq/fv38fly9fxoEDB6CtrQ13d3d07tyZp0Cxr+InQaYSiiDEy8sLFy9eREBAAO7fv49Lly7h9evXGDt2LE6dOqV0TGhoKMaOHYt58+Zh/PjxHMSzAkkul0NXVxelSpXClStX8M8//yh9p7BlyxalB9nccz85iP/96OrqolmzZggODkZKSgq6d++Oe/fu5dlPKpUCANzc3AAotynG1AX9fy6b6OhoNG7cGLa2tggJCcHt27dRu3ZtrF27Fn379hX3r1GjBvbv3483b96gf//+OH36tApLz9jXCYKAGjVqYOPGjcjJycGQIUNw+fJlcdSUog81933dzc0N/fr1w/79+7Fz506MHTuWg3j2Tfw0yFTm9u3buHbtGsaOHQtvb2+UKlUK+vr6iIuLQ9++fZWGjj558gRbtmxBVFQUgoODMW7cOAD8kMrUm0wmy7NNIpFAU1MTbdu2xZMnTxAaGor4+HjxOwCIi4vD0aNHERYWhqSkpHwtM1NfOjo6aN68OYKCgpCSkgIvLy+lYJ6IxAfDLVu2wMbGBrVq1VJVcRn7IkEQkJqaismTJ6Ns2bKYPXs2+vTpg7Jly8LQ0BAZGRnYvn07vL29xWNq1KiBbdu2QUdHB8WLF1dh6Rn7MkWQ/mkwP2jQIJw8eRKCICAyMhKbNm3Czp07ceDAARw9ehTh4eHYs2cPIiMjlc7D2Ndwek+WbxRz4RSePXuGx48fo06dOjA0NMTp06fh6emJDh06YOLEibC1tRX3K168ONavX4/s7GzxDSUPL2bqLPeQuc2bN+Px48fQ1NREuXLl4OnpicGDB+P69etYvHgxcnJy4OXlhYoVK+LBgwf466+/sH//fixdujTP/Hn2e1ME8wAwYsQIeHl5YevWrShTpow41Pj8+fO4fPkyPD09YWhoqMriMqYkdx6HjIwMREREoH379qhevToAYMKECVi1ahUWLFiAu3fvYsOGDdDV1cWaNWsAAHXq1MHdu3fFnDmMqRPFc+67d++QlpYGe3t71KxZExs3bkSPHj3g7e2NlJQUrFy5Ert370ZSUhKkUil0dHQglUqhq6srvpzlqSPsu6ggwR77DeXOSjt//nzKycmhEydOkLGxMcXFxdGlS5dIT0+PunfvTq9evRL3Xb9+PfXp04eSkpKUzsfrZzN1lru9e3p6krGxMRUuXJj09fXJysqKOnfuTEREKSkpNGTIEBIEgczMzKhy5cpka2tLenp6NHfuXPEc3N7ZpzIzM2nv3r3k4OBAZcuWpQcPHhAR0YMHD6hFixZUvHhxio6OVnEpGSOKjY2lR48e0b179/Jcy65duyZeL1esWEGampoUEhJCREQvX76kQoUKkb6+vtJa8oypI0V2+sjISKpbty5VqVKFrl69SkQf7+EXLlygSpUqkbm5Oa1evZqio6MpKiqKIiIi6MaNG3Tu3Dl6/vy5KqvACiAO5Fm+6tq1KxUuXJiOHj1KaWlpZGtrS9WqVSMDAwPq3bs3PXv2TNz38ePH1KhRI+rYsWOeQJ6xgmDgwIFkZWVFmzdvpjdv3lB2dja1aNGCBEFQWmZm69atNGDAAKpduzaNHDmS9uzZI373taWZ2O/t02B+//791KpVKzIyMqLbt2+runiM0a5du6hcuXKko6NDgiBQ37596cqVK3n2i4uLo9q1a1O3bt0oISFB3F6qVCmqXbs2lSpVil68eJGfRWfsuyleUP3zzz9UqFAhatKkCS1atEhpH6lUShcuXCAnJycqW7YsnTlzRhVFZb8YDuTZT5U7CElISKBy5crR2rVrxcB88eLFZG1tTUWKFKHXr1+L+z579oymTJlChQsXpu3bt+d3sRn7nyUkJFCFChVo/Pjx9P79eyL62Pukr69PPj4+9OTJkzzH5OTkKP3MQTz7FkUw7+zsTIIgkJGREd26dUvVxWKM1q9fTxKJhJo2bUojR46kzp07k0QiodatW+cJyuPi4qhw4cI0evRocVtYWBjVqFGDLl68qPR8wJg6ev/+PTVr1ozc3d3p2rVr4vbc68greuadnJzIzc2Njh49mmedecb+DZ4jz34qxRz2Tp06wcrKCkZGRmjTpg1MTU0BfMxa/+zZMyxfvhxt27aFl5cXcnJyEBYWhiNHjmDWrFno3LkzAOW5dYypm0/b55s3b3Dnzh1MnjwZRkZGCAsLQ8uWLdGuXTtMnToVNjY2AD7OZ65duzYA5Qy2AGenZ9+mmDOfmZmJVatWITg4+KvL0jGWH0JDQ9G3b1+MHj0aw4YNQ7FixfD69Ws4OTlh3rx58PLyQpcuXcT9NTU1Ub58eRw4cABNmjRBdnY2li9fjnfv3qF06dKcK4SpvczMTNy4cQM9e/ZElSpVAHx8LlDkhlI8I3h4eCA0NBSenp6YOnUqateuDX19fVUWnRVgHMizn+7Ro0d4+vQp9u7dC2tra7x58wbm5uYAAGtra0yePBmlSpXCsmXL4O/vDyJCpUqVEBwcDB8fHwCc2I6pt8+1Tw0NDZibmyMnJwcXLlxAy5Yt0b59eyxcuBBFihQBAOzatQvBwcFYs2YNXFxcVFF09gvQ0dFB27Zt0bJlSxgZGam6OOw3d/LkSfTu3Rvdu3eHn58frK2tAQCFCxeGh4cHAOD58+dKxxQqVAi9evXCwoUL0bx5cxgaGsLExASHDx/mIJ6pPSLCs2fP8ObNG5QvXx4AkJ2dDW1tbQDKzwiKYP7IkSOwsLDgIJ79TziQZz+ds7MzlixZgsDAQOzbtw9HjhxBqVKlQESQy+UoXLgwBg8ejP79++Pu3bswMjKCqakpChcuDICDeKb+FO2zc+fOsLe3x+LFi+Hi4gIPDw/4+voiNTUVXbp0wezZs8UgPjY2Frt27YKmpiZnFmf/M11dXc7kzVROLpcjKSkJDg4OiIiIQHJyMqytrcVs3tra2rCwsMD58+fh5OQEGxsblCpVCqampujRowfKlCmDe/fuIScnB40aNYK9vb2qq8TYNwmCAGdnZzg6OmLTpk3o1q0btLW1IZfLIQiC+IwwadIkNGrUCPXr10e1atVUXGr2K+BAnv1QXwq6FUOHU1NT4efnBysrK3h5eSkdp6WlhUqVKikdR0QcxLMC4c2bN4iOjsarV6/w6tUr2NjYYPTo0UhISMCtW7fg7e0NS0tLAEBMTAw2bdqEI0eOIDg4WFxqkTHGCjKJRIJWrVpBW1sbvr6+6Ny5M0JDQ1GhQgUkJCRg+PDheP/+PW7evIkzZ84gNTUV5cqVQ7169TB48GDY29ujQoUKqq4GY1/0pedcPT09NGjQABs3bsTChQsxatQopZerV69exc6dO6Grq4u6devysy37IQQiIlUXgv0acq+bffXqVSQnJ8POzg6lSpUSL1gXLlzAjBkzcPr0aWzZsgVdu3ZVZZEZ+6EOHjyINm3aYMOGDejZsyeysrKwY8cOBAQE4N27d2jatClMTU0RERGBiIgITJ06FePHjwfAOSAYY7+OrKwsHDlyBCNGjIC5uTmCgoIwaNAgGBgYYO7cuahTpw4iIyNx69YtrFixAvfv30daWhrq1auHo0ePQktLi6+HTO0oRpbExcXh1q1bePLkCRo3bozChQvD1NQU79+/R61atfD48WMMHDgQEyZMgJGREc6dO4fFixfj4cOHOH36NBwdHVVdFfaL4ECe/RCKixsAdO/eHcePH8fbt2+hra2NwYMHo0+fPihXrhwA4OLFi5g+fTpOnz6NrVu3KiW8YawgyP1GXvGZiMQh9E+ePMHff/+N4sWLIycnBxEREVi+fDkuXryI9PR0eHh4oE2bNujRo0ee8zHG2K9AEcyPGjUKz549Q+nSpbFz506ULl0agiCI172kpCQkJCRgzZo16Nu3L9zc3FRddMbyULTXBw8eoF27dnj58iXS09NhZGSEAQMGoG/fvnB1dUVycjLatGmDK1euwNjYGPr6+sjIyIC2tjYOHz4sPgsz9iPw0Hr2QyiC+A4dOuDChQsYMWIEKlSogCdPnmDEiBF48+YNhg0bhmrVqqFmzZqYPn06NDQ00K1bN2RmZqJXr14qrgFj3+fT6R65E9goVmXw9fXFlStXULx4cWhpaaFq1aqoWrUq3r59C0EQYGho+NkkOIwx9qtQrKggl8sxY8YMpKWlQSaTiT3tgiCAiGBmZgYzMzMsXrxYxSVm7MskEgliYmLQpEkTlCpVClOnTkX9+vURHByMZcuWIT4+HmPHjkW5cuVw5MgR7Ny5E9evX8ebN29QqVIldOrUCcWKFVN1Ndgvhnvk2Q+zbNkyLFmyBLNmzULbtm2hr6+PO3fuoEKFChAEAa1atcKkSZPg7u4O4OOyW35+fvDy8sKIESNUXHrG/p2uXbvi/v37mDFjBmrUqCEmsQOAWrVqIS0tDREREQCUR6zkxsPpGWO/uszMTBw9ehQjRoyAqakpNm/ezEsksgInPT0dQ4cOxZMnTzB//nwxWV1AQACmTp0KIyMjtGjRAlOmTIGrq6uKS8t+F9wNxH6IrKwsvHr1Ci4uLmjQoAH09fURHh6OGjVqYMCAAQgMDMSBAwewYMECXLlyBcDHBHj79u3jIJ4VOO/fv0e5cuWgp6eHnj17wtPTEwcOHEB8fDwAwNfXF1FRUQgODgaAzwbxADiIZ4z98nR1ddG8eXMEBQUhOTkZ3bt3x/3791VdLMa+SSaTKX1+//49KlWqJAbxkyZNwsyZM7Fp0yb4+Phg+/btCAgIwIMHD5TOw32m7GfhHnn2wxw7dgzFihWDi4sLoqOjUa1aNdSvXx+BgYGws7ND69atcfLkSTRv3hx+fn6oUaOGeCz3TLKCavHixTh48CDOnTuHBg0aoHv37ujUqROqVasGR0dH7Nq1Czo6OqouJmOMqZRizvyYMWOQnZ2N48ePo3Tp0qouFmNKXr9+DSJSWmVGMaT+ypUrYhC/evVqDBkyBH/88QeGDBkCDQ0NVKxYEW/evEGFChWwePFiODs7q7Iq7DfAPfLsX8v9hjK3pk2bwsXFBTKZDKtWrYKNjQ1mzJgBOzs7AICNjQ1sbW2xd+9eJCYmKh3LQTwraORyOQBgzJgx2LdvHzZt2oR3796hX79+8Pb2houLCw4fPowdO3aouKSMMaZ6Ojo6aNGiBebMmQMzMzN+wcnUTnJyMhYsWICxY8fizZs3ePLkCSpXrozjx48jOztbDOLfvXuHnTt3onPnzujRowc0NDQgk8kgkUhgbGyMe/fuwcDAQMW1Yb8DTnbH/pXcS8ydP38esbGxsLOzg7W1tfjmUUNDAzExMdDQ0EDx4sUBAM+ePUNMTAxWr16NUqVKKc0nZkxdfDoyJHd7/5QiU70gCDA1NUX37t1Rp04d3Lx5EzNnzsS9e/cAfJwfyhhjDNDW1ka7du3QsmVLGBoaqro4jCkxNTWFkZERFi9ejPj4eFy6dAm1atVCnz59xAS1AKClpYXHjx/D2toaZmZmAD4ur2xgYIADBw7AwMAApqamKqoF+51wIM++m1wuF4OaDh064PTp00hJSQEAlClTBqNGjUKfPn0AfLwYPn36FH///TcsLS0RFhaG69evIy0tTQziOVs3UzeCICArKwsvXryAk5OT2N43bNiAXr165Rk5ovhZEdDb29vD3t4erVq1wurVq2FlZYV27drlez0YY0xdaWtrKwVFjKla7oS0U6dOxbNnz7B582ZYWFhg0KBB4pKIuV/229ra4vLly1i3bh0MDAywYcMGvHjxAhoaGhzEs3zDc+TZv+bl5YWwsDBMnjwZLVq0QExMDIYOHYqoqCicP38eNWvWRGpqKurVq4ebN29CT08PEokEkydPxrhx41RdfMa+6vjx45gwYQL8/f3RtWtX1K9fH5cvX0ZERARKlSr1zeM/l6GeX1oxxhhj6ufmzZs4duwYateujVq1agEAKleujCdPniA5ORkdO3bErFmz4OLiAuA/9/ioqCi0aNECz58/h6amJqytrbFv3z5ekYHlK+6RZ//K3bt3ceXKFfj5+cHb2xvGxsZ4+/YtYmNjMWDAADg4OEAul8PQ0BBXrlxBUFAQ9PX1UbRoUTRv3hwABzVMvRUpUgSWlpbo3r075syZg3fv3mHr1q3iNJFv+VyGem7vjDHGmHrZtWsXpk6dipSUFLx//x41a9aEIAg4e/YssrOzsWTJEsyZMwdEhBkzZsDV1RWCIEAul6NkyZK4cOECLl26BJlMhurVq4s5oRjLL9wjz/6VQ4cOoXXr1rhz5w7KlCmDU6dOoXXr1mjbti0WLFgAW1tbAEBUVBRKliyZ53gO4llBcOfOHXh4eCAjIwMjRozAkiVLVF0kxhhjjP0g27ZtQ+/evdGpUyf06tULjRs3BpB3VN3EiRMxb948dOjQAdOnTxeH2T99+hQfPnzgHnimUtwjz/4VXV1dmJqaQldXF2fOnEGrVq3Qvn17LFy4UJz7vmnTJhw9ehRBQUEoVKiQ0vEcxDN1lfvmffv2bdSqVQtxcXEIDg6Gu7s7unXrpuISMsYYY+x/df36dUyaNAkDBw7EmDFjUKxYMQBAdnZ2nvwNih75+fPnQxAETJo0CQYGBvDz80NGRgZ27twJY2NjFdSCMQ7k2Rd8qefc3d0dEokEAwcOxLVr19ChQwfMnz9fDOJjYmKwYcMGWFpaQktLK7+Lzdh/TRHEN2rUCG/fvsVff/2F5ORkzJ49G97e3sjJyUHPnj3zZLZnjDHGWMFx9+5dpKWloVu3bmIQD0AM4h88eAAigoODA4yMjDB37lwIgoBFixbh3LlzMDY2xqtXr3Dx4kUO4plKcfcoy0MqlYpBfGRkJGJiYsS1401MTDBjxgzcvXsXZmZmmDVrFmxsbAAAz58/R2hoKO7cuYN27drBxMREZXVg7HtJpVLx859//omnT59iyJAhcHBwQLVq1TB16lQ0a9YMffv2xcaNG8UgPjk5GefPnxfXk2eMMcaY+rtx4waysrLg4eEhbouPj8eePXtQpUoVlClTBmXLlkXv3r2xb98+AB975oOCgtCoUSNUqlQJ165dQ/ny5VVUA8Y+4h55poSIxCW3unbtimPHjkEikcDNzQ3r1q1DiRIl0K5dOzx//hx//PEHfHx84OnpCW1tbRw9ehR///03Zs2ahc6dO6u4Jox9H0V7P3LkCBITE1GmTBl4e3tDV1cXAFClShXMmDEDANCvXz8IgoAqVarg4MGDmDBhAk6fPo169eqpqviMMcYY+xfKli2L9PR0zJ49G5MmTcLVq1cxf/58/P3337CyskKrVq2gq6uLffv24dmzZ7C2tkb16tUxePBgDBo0CDKZTHx2YEyVONkdE+UeMjxo0CDs2bMHPXr0gCAI2L9/PzIzM7Fu3To0adIEiYmJOHjwIAICApCQkACpVAp3d3f06tULAwcOBMCJ7VjBsXbtWgwcOBDGxsbo0aMHQkJCACi34Rs3biAgIAD79++HjY0NXr9+jYkTJ2LatGmqLDpjjDHG/oXnz5+jU6dOuHXrFgoXLoyEhATIZDL06NEDEydORIkSJaCpqYng4GCMGjUKy5Ytw6BBg1RdbMby4ECeAVAO4pOSktClSxc0a9YMI0aMgIaGBm7duoWRI0fiwYMH2LRpE5o2bQoAyMjIwJMnT6CpqQkLCwtYWFgA4CCeFSwxMTFYvXo1goODUbp0aezduxdFixYFoNyWnz9/jkOHDuHu3buoV68eunTpkmcfxhhjjKm3J0+eYNWqVbhx4wZKly6N6tWrw8vLS2mfO3fuoEKFChg8eDCWLVumopIy9mUcyDMlHTp0QGpqKlJSUrB582aUKFFC/C4qKgoDBgxAZGQkQkNDUb9+/TzZPQFwMjCm1r4UdEdHR2PFihUIDAzE6NGjMWPGDOjr63/2GKlUKg6r4yCeMcYYK3jkcjlkMplScuacnBzx582bN2P48OEICgqCt7e3qorJ2Bfx0ycTJScnw87ODuHh4bhx4wYiIyOR+z1PyZIlsWbNGpQuXRr9+vXDqVOnPpvoi4N4pq5yJ3JMTk7G06dPkZSUBAAoUaIEhg0bhiFDhmDx4sWYO3cuMjMzAeRdNjH33DgO4hljjLGCRyKRfDGIv337NtauXQtbW1s0aNBAVUVk7Kv4CZSJTE1NMXbsWPj7+0NTUxO7du1CSkqK0j4lS5bE2rVrUbRoUbRp0wYvXrxQUWkZ+3dyJ6cZPHgwatWqBUdHR3h4eGD06NEAgOLFi2Ps2LEYOnQoZs+ejdmzZ4vBPGOMMcZ+XYogfvfu3Rg/fjzu3r2LrVu3wtbWVsUlY+zzOOXib0Imk0FDQwM5OTnQ0ND4Yi+ira0t+vXrh+zsbMyZMwfGxsaYM2cOjIyMxH1KlCiBP//8E/fu3YODg0N+VYGx/4linfg2bdogPDwczZo1Q7du3fD3338jMDAQ9+7dw4YNG2Bvb4/Ro0dDEATMnz8fMpkMU6ZMgZ6enoprwBhjjLGf5dKlS5gyZQqioqJgZWWFc+fOwc3NTdXFYuyLeI78byQ+Ph6+vr74448/YGdn9819Q0JCMHfuXAwZMgRz586FoaHhZ/flOcKsoNi+fTv69++PoKAgdO7cGYaGhoiLi8OOHTswbdo0VK9eHUePHgUAPH36FAsWLMDKlStx8eJF1KhRQ8WlZ4wxxtjP8vTpU0ycOBHVq1dHx44dYWNjo+oiMfZVHMj/BhSBdo8ePXD27FnExsZ+13G5g3lfX1/MmjVLqWeesYImICAAc+bMQXR0NGxsbMSRKsnJyVi7di3Gjh2LKVOmiOvGP336FE+fPuV14hljjLHfQHZ2NjQ0NMRRfIypMx5a/xtQ9JZrampCX18f6enpYjbur7G2tsawYcMgkUgQEBCA9+/fY9WqVUqJQRgrSLS0tJCZmYnU1FQA/0nMaGpqii5duuDPP//EuXPnxIQ3xYoVQ7FixQDwyBPGGGPsV/e51ZgYU1f8VPobyD3o4t8OwLC2tsbgwYMxcuRIlC9fnoN4ViB8upqCot27uLgAAEJCQpCUlASJRAKZTAYigr29Pdzd3REdHY309PQ85+QgnjHGGGOMqQvukf8FKXoOFf9VrOsuk8mgra39r4PxIkWKICAgQOzF53XimTrLvcb7+/fvkZ2dDVNTU2hqaqJt27Zo3bo1Vq9ejaJFi6J3796wsLAAAMTGxuLx48coX748J7ZjjDHGGGNqjbuYfkESiQRv375F69atcevWLaWh9Tk5OZBKpf/6nLmH4nMQz9SVXC4Xg/gBAwbAw8MDzs7OaNOmDVasWAHgY8I7Dw8PjB8/Hn5+foiIiMC5c+cQEhKCq1evon379jy0jjHGGGOMqTXukf9FRURE4NSpU3j16hVCQ0NRpkwZaGhoIDs7Gzk5OUo9jkQEIhIDfp4LzAqi3G24Y8eOOH36NOrWrYuaNWvi2LFjOHLkCB48eIClS5fixIkT8PLywl9//YWNGzcCAMzMzBAQEIC+ffuK5+OXVowxxhhjTB1xIP+Lql27Nv766y8MHz4cXbp0wf79+yEIAszMzKCtra0UrAuCoBSwcBDPChpF9nkAiI6Oxtu3bxEUFIR27drB0NAQd+7cwYoVK7Bs2TJoaGggMDAQ27dvx6VLl/DkyRPo6+vD1tYWVatWBcAvsxhjjDHGmHrj5ed+YVlZWThy5AiGDRuGIkWKwMTEBKdPn0aLFi2QkZEBQ0NDGBoaQhAE6OnpwdTUFHK5HJmZmZg3bx4vNccKnJ49e0Iul+P48eO4ePEiSpYsKfasP336FLNnz8aff/6JDRs2oGfPnp89BwfxjDHGGGNM3XEg/4tTBPOTJk1CZGQkihYtikKFCiEzMxMZGRnIysoCESE7OxtZWVnIzMzEzJkzMW7cOFUXnbF/RS6Xo1ixYnjx4gWsrKxw7do12NnZKSW/u379Oho3bowOHTpg7dq1PHyeMcYYY4wVSBzI/wYyMzPx999/Y8aMGZBKpdi8eTMqVqyIzMxMZGZmQiKRiEG9VCqFo6MjAJ4jzAoOxbrvUqkUjRs3xtmzZ9GvXz/88ccfMDQ0FL8HgEaNGiEhIQFXr17l7PSMMcYYY6xA4jnyvwFdXV20aNECgiDA19cXvXr1wtatW1GmTBno6uoCAIyNjZWO4eHFTJ3lnhMPQAzSNTU1ceLECdSqVQt//vkn7O3tMXr0aBgaGgIAnj17hsTERBQtWlTspWeMMcYYY6yg4SfZ30TuYH7EiBHw8vLCtm3b4Obm9tn9OYhn6ip3EL9y5UrEx8cjJycHXbp0gb29PczMzHD+/HnUqFED06dPxz///IOpU6ciJiYG4eHhePDgAXx9fcXgnzHGGGOMsYKGh9b/ZhRz5seMGQMtLS1s374d5cuXV3WxGPvXPD09cfLkSWhrayMnJwfAx7Xj+/fvj3LlyiEnJwd169bF5cuXYWlpCUEQUK1aNdSqVQt+fn4AePoIY4wxxhgrmLhH/jejo6OD5s2bQy6Xo0+fPrh37x4H8qxAyJ20buXKlbh16xZWrFiB+vXrIykpCWvWrEFISAhevXqFadOmoWzZsjh37hwaNGiAS5cuwd/fH8OHD0eRIkUA8PQRxhhjjDFWcHGP/G8qKysLL1++FBPbMVZQHDx4EHFxcTh58iRCQ0PFPA8AMGXKFMyePRtjx47F1KlToa+vD6lUiurVq4tD7IcNGwZ9fX0O5BljjDHGWIHFgTzjgIYVGCtXrsSQIUNgYGCALl26YO3atQCU58336tUL+/fvx82bN8UXVVKpFDVq1MC9e/cQEBCAQYMGwcDAQGX1YIwxxhhj7H/B0RvjIJ6pLZlMpvRzrVq1MHToUGhoaCAiIgKJiYkAAA0NDUilUgCAl5cX3r9/jyNHjgAAsrOzoampifDwcFSsWBH+/v7iGvKMMcYYY4wVRBzBMcbUlqKX/caNGwCAMmXKYMiQIfDy8kJERARmz54t7quYP6+trQ1NTU0YGRmJPyvm1587dw7169dHkyZNOMkdY4wxxhgrsDjZHWNMrfXu3Rt///031q9fj5YtW6J06dIYMWIEZDIZli5dCrlcjlGjRsHR0RHPnz/HkSNHoKGhATs7O/EcmpqaYjB/6tQpFdaGMcYYY4yx/x3PkWeMqbV79+6hXr16sLW1xZw5c9CyZUsAwKNHj7BkyRKsXr0ajo6OKFWqFD58+IB79+7B398f48ePV3HJGWOMMcYY+zk4kGeMqY1P13XPycmBlpYWHj58iBo1asDW1hbz5s0Tg/mHDx8iODgYmzZtgrGxMUJDQyEIAurXrw+AEzkyxhhjjLFfEz/hMsbUglwuF4P49PR0AICWlhakUilcXFwQHh6Oly9fYvz48Th8+DAAwMXFBUOHDoW3tzdevXqFixcvikG8VCrlIJ4xxhhjjP2SeI48YyzffdrzTkRi0N2tWzdUqFABgwYNgomJiTi/3cXFBZcuXYKHhwcmTZoEmUyG1q1bw9XVFcOHD0dOTg6mTZuGtLQ0zJs3T0x+xxhjjDHG2K+Gn3QZY/lKMdw9NTUVJ06cgIODAypXrgzg43JzycnJmDlzJoyMjNC9e3elYL5UqVLYunUr2rRpg6CgIMhkMrRr1w4uLi4YN24cBEHAwoULkZaWhqVLl6q4powxxhhjjP0cPEeeMZZvFEH869ev0bdvX0RERMDFxQWHDh2Cnp4egI/z4vv06YPdu3dj0aJF6NGjB0xMTMRe/MTERNSvXx+RkZGwt7fHmTNnULx4cQBATEwMpk6dioMHDyI6OhqWlpaqrC5jjDHGGGM/BffIM8byhWL4fGJiImrUqAFjY2OMHj0aAwYMEIN44OO8+PXr14OI4OfnBwDw8vKCmZkZACA+Ph7FixfH4sWLERsbKwbxAODk5ISAgAAsWrSIg3jGGGOMMfbL4kCeMZYvBEFAamoqvLy8YG1tjXnz5qFmzZqQSCRidnoFLS0tbNiwAXK5HGPGjEFSUhL69OkDDQ0N7Ny5E0+ePEGNGjXQrFkzAMpz7nMH9owxxhhjjP2KeGg9YyzfXL58GW3atEFAQAD69u0LDQ0Npe/fvHkDmUwGKysrcVv//v2xbt062NnZQUdHBzExMVi4cCHGjBmT38VnjDHGGGNMLXAgzxjLN2vWrIGPjw/evn0rDpWXyWR48OABNm7ciL/++gvp6elo3749OnTogObNm4vHXb16FcnJyfD09ESvXr0A5M1+zxhjjDHG2O+AA3nGWL45evQoWrVqhWnTpmHy5MnIysrCggULsHnzZjx58gTFixeHsbEx7ty5g9KlS2Px4sVo2LCheLwiWd6nnxljjDHGGPud8Bx5xli+KV++PKpUqYIFCxbg+PHjePPmDf755x8UL14c27dvR7Vq1WBmZoaNGzdi2LBhOHfunFIgr+h9z73uPGOMMcYYY78b7pFnjOWrmJgYzJgxA5cvX4a5uTlq166NiRMnikPtAeD169eoUKEC7O3tcfHiRUgkEh5CzxhjjDHG2P/jHnnGWL5ycnLCmjVrkJ6eDkNDQ2hoaEAikUAqlUJT8+Ml6cWLFyAi1K1bN09CPMYYY4wxxn53HMgzxvKdjo4OdHR0xJ9zB/EJCQnYsmULsrOzUa9ePRWVkDHGGGOMMfXFgTxjTOUUQfydO3ewfft2BAcHY86cOWLWesYYY4wxxth/8Bx5xpjKPXv2DOvWrcPu3bvx4cMHjBo1CiNHjgTA2ekZY4wxxhj7FPfIM8ZUTktLC2fPnkWNGjXQoUMHNGvWDAAH8YwxxhhjjH0O98gzxtRCamoqAMDQ0BDAxyXmOFM9Y4wxxhhjeXEgzxhjjDHGGGOMFSA8ZpUxxhhjjDHGGCtAOJBnjDHGGGOMMcYKEA7kGWOMMcYYY4yxAoQDecYYY4wxxhhjrADhQJ4xxhhjjDHGGCtAOJBnjDHGGGOMMcYKEA7kGWOMMcYYY4yxAoQDecYYY4wxxhhjrADhQJ4xxhhTY2fOnIEgCBAEQdVF+a/Vq1cPgiBg+vTpn/0+PT0dU6ZMQenSpaGnpyfW99atWwCAYsWKQRAEbNiwId/KzBhjjKkzTVUXgDHGGPtdyGQy7N69G4cOHcLly5eRmJiI9PR0mJqawtnZGbVr10b37t1RpkwZVRc1X3Xp0gWHDh0CAOjp6cHKygoAoKWlpcpiMcYYY2qLA3nGGGMsH1y+fBm9evXCo0ePxG1aWlowMjLC27dvcfHiRVy8eBHz5s1D+/btsW3bNmhra6uwxD+Og4MDXFxcUKhQoTzf/fPPP2IQv337dnTu3DnPPk5OTtDV1YWJiclPLytjjDFWEAhERKouBGOMMfYrO3jwIDp16oSsrCxYWFjAz88PHTp0QMmSJQF87KmPiIjA7t27sXz5crx//x5JSUkwNTXFmTNnUL9+fQDAr3jL3rlzJzp37gwLCwu8efNG1cVhjDHGCgTukWeMMcZ+oqioKPTo0QNZWVlwdXXFsWPHYGdnp7SPhoYGqlSpgipVqsDf3x99+/ZVUWnzX3p6OgDA0NBQxSVhjDHGCg5OdscYY4z9RJMnT8b79++hq6uLvXv35gniP2Vubo59+/Z91zByuVyOU6dOYfjw4ahevTrs7Oygra0NCwsL1K1bFytXrkROTs4Xj09KSsLUqVNRqVIlGBsbQ1tbG9bW1ihXrhwGDRqEU6dO5TkmIyMDixYtQo0aNWBmZgYtLS0ULlwYrq6u6NWrF3bv3p3nmM8lu5s+fToEQUDv3r0BAM+ePROT3OXeDnxfsruLFy+iR48eKFq0qDgMv2rVqpg/fz5SU1M/e0zv3r3F30VEWLt2LWrVqgULC4s8v+/FixcYNWoU3NzcYGBgAB0dHdjY2KBy5coYNWoUrl279sWyMcYYYz8a98gzxhhjP0lCQgJ27doFAOjevTucnZ2/+9jvyVL//PlzNGrUSPzZ0NAQ+vr6ePfuHc6dO4dz585h69atOHbsGPT09JSOffHiBWrWrInnz58DACQSCUxMTPDmzRskJCTg7t27+Oeff9CwYUPxmA8fPqB27dq4ffu2WEYTExMkJyfjzZs3iIyMxNmzZ9GhQ4dvlt3Q0BBWVlbIyMjA+/fvIZFIULhwYfH7750PL5fLMWrUKAQHByudOy0tDdeuXcO1a9ewfv16HDt2DEWLFv3sOYgInTp1wu7du8W/B4nkP30dt2/fRv369ZGUlATg4wgKY2NjxMfHIy4uDjdv3kRSUhJn1WeMMZZvuEeeMcYY+0nCwsIgl8sBAO3atfvh59fU1ET37t1x4MABvH37Fh8+fEBycjI+fPiA9evXw8bGBufPn8ekSZPyHDt9+nQ8f/4cxYoVw8mTJ5GdnY13794hKysLT58+xYoVK1C9enWlY4KCgnD79m2Ym5tj9+7dyMjIQFJSErKysvDy5UuEhoaiSZMm31V2Pz8/xMfHIygoCABgb2+P+Ph48Y9i+7dMmzYNwcHBsLS0xLJly8S/h4yMDISFhaFixYp4+PAh2rdvL/6/+NSePXuwf/9+LFq0CElJSXj37h1SUlLQtGlTAMCYMWOQlJSESpUqITw8HDk5OXj37h0yMzPx6NEjLFq0CG5ubt9VXsYYY+xH4B55xhhj7Ce5f/+++LlixYo//Px2dnbYvHlznu2Ghobo3bs3ypQpA3d3d6xevRpz5syBrq6uuM+lS5cAAHPmzFHqddfQ0EDRokUxaNCgPOdVHOPn54f27duL2yUSCWxsbODt7Q1vb+8fVr9vefr0KebOnQs9PT0cP34c5cuXF7/T0tJCvXr1cPbsWbi6uuLmzZs4cOAA2rZtm+c8qampCA4Ohq+vr7jN0NBQnLevqHdISIjSyw1tbW2ULFkSY8aM+Uk1ZIwxxj6Pe+QZY4yxn+Tt27fiZ3Nz83z//VWqVIGlpSXS0tJw69Ytpe9MTU0BAHFxcd99vv/mmJ9pw4YNkMlkaNasmVIQn5uRkZEYvB87duyz+5iZmcHHx+eLv0fd6s0YY4xxIM8YY4wVYNnZ2Vi5ciWaNGkCGxsb6OjoKCWNS0xMBPBxTnxunp6eAIDx48dj4MCBOHr0KN6/f//V36U4JiQkBN26dcO+fftUumTcxYsXAQDHjx+HtbX1F/+sX78ewMeEep/j7u4ObW3tL/4eRb179eqFMWPG4OzZs2K2fcYYY0wVOJBnjDHGfhILCwvx87t37374+RMTE1GlShUMHjwYJ06cQFxcHCQSCQoVKgQrKytYWVmJSdvS0tKUjvX390fnzp2Rk5ODNWvWoHnz5jA1NUXZsmXh7++Phw8f5vl9Xl5eGDFiBARBwF9//YV27dqhcOHCKFmyJIYOHYobN2788Dp+zatXrwB8rFtCQsIX/yjq/qXg29LS8qu/Z8GCBahfvz5SU1OxZMkS1KtXD8bGxqhSpQqmTZuGly9f/tiKMcYYY9/AgTxjjDH2k+ROgBYREfHDzz9q1CjcvXsXFhYWWLduHeLi4pCRkYHXr1+LSeNsbGwAfMzMnpuWlha2b9+OW7duYerUqWjQoAH09fVx7949MXnb4sWL8/zOwMBAPHz4EHPmzBGD/+joaCxfvhxVqlTByJEjf3g9v0QmkwEAxo0bByL65p8zZ8589jwaGhpf/T2mpqY4ffo0zp8/j7Fjx6JmzZrQ1NTEjRs3MHPmTJQsWRLbtm370dVjjDHGvogDecYYY+wnqV+/vtgjvnfv3h967pycHOzZswfAx6Huffr0gbW1tdI+Mpnsm0Pfy5cvjxkzZuDUqVNITk7GyZMnUadOHchkMvj7+4tLzeVWokQJTJgwAX///Tfevn2L8PBwcR56UFAQDhw48GMq+Q2K+n5pyPyPVqtWLcyfPx8XLlxAcnIy9u/fj7JlyyIjIwN9+/ZFQkJCvpSDMcYY40CeMcYY+0msrKzENdW3bt2KR48effexn/agf+r169fIzMwE8OWM+BcuXBD3+R6amppo2LAhDh8+DB0dHRARTp48+dVjJBIJqlevjl27dsHBwQEAcOLEie/+nf+LmjVrAgBOnjz5r+r5I+jq6qJ169biy5TMzExcuHAhX8vAGGPs98WBPGOMMfYTBQQEwNDQEBkZGWjfvv0351MnJSWhQ4cOSElJ+ep+xsbGEAQBAD7bay6VSj+7frxCVlbWF7/T0dERh5srRhR86xgNDQ0xYVzuY36mvn37QlNTE2/evMG0adO+um92djZSU1P/9e+QSqVfXH8eAPT09MTP+VVvxhhjjO84jDHG2E/k7OyMTZs2QVtbG/fv30eFChUwf/58REdHi/vIZDJERERg6tSpcHR0FHt5v8bQ0FDskR49ejROnz4tBpz37t1DixYtcP36dRgYGHz2+KJFi2LChAm4fPmyUoAeHR2N7t27Iz09HRKJBE2bNhW/q1atGoYPH44zZ84oJc979eoVfH19xTq1aNHiX/wN/fecnJwwZcoUAB8T0vXs2RP37t0Tv5dKpbh16xZmzpyJEiVK5FmC73u8ePECJUuWREBAACIiIiCVSsXv7ty5gx49egAADAwMULdu3f+tQowxxth30lR1ARhjjLFfXdu2bXH69Gn07t0b0dHRGD9+PMaPHw9tbW0YGhoiOTlZDMIFQUC3bt2+GIDnFhgYiLp16+Lly5do2LAhdHR0oK2tjQ8fPkBTUxPr1q3DlClT8mSsB4CEhATMmzcP8+bNg0QigYmJCTIyMsQh6oIgYPHixXB1dRWPSU5OxtKlS7F06VIIggATExPk5OQonX/UqFFKwf/PNmXKFEilUgQEBGDTpk3YtGkT9PT0oK+vj+TkZDEhnqJO/43Hjx9jypQpmDJlCjQ0NGBiYoLU1FRkZ2cDALS1tbFhwwaYm5v/kDoxxhhj38KBPGOMMZYPatasiX/++Qc7d+7EoUOHcOXKFSQmJuLDhw8wNzdHqVKlULduXXh7e8PFxeW7zlm5cmVcvXoVM2bMwOnTp5GSkgIjIyM0b94cfn5+cHd3F3usP3X8+HGEhYXhwoULeP78uZiorUSJEqhduzaGDh2KypUrKx3z119/4fjx4zh37hyePHmC+Ph4SKVSFC1aFNWrV8fAgQPRoEGD/+0v6l8SBAEzZ85E586dsWLFCoSFhSE2NhYpKSkwMzODs7MzatasiXbt2qFGjRr/+vy2trY4cOAAwsLCEB4ejhcvXiAxMRGampooUaIE6tevjxEjRqBkyZI/oXaMMcbY5wn0rWw6jDHGGGOMMcYYUxs8R54xxhhjjDHGGCtAOJBnjDHGGGOMMcYKEA7kGWOMMcYYY4yxAoQDecYYY4wxxhhjrADhQJ4xxhhjjDHGGCtAOJBnjDHGGGOMMcYKEA7kGWOMMcYYY4yxAoQDecYYY4wxxhhjrADhQJ4xxhhjjDHGGCtAOJBnjDHGGGOMMcYKEA7kGWOMMcYYY4yxAoQDecYYY4wxxhhjrADhQJ4xxhhjjDHGGCtA/g97EX08hq53FgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "colors = cm.rainbow(np.linspace(0, 1, 4))\n",
        "labels = ['K nearest neighbors','Support vector machine','Decision Tree', 'Random Forest','Stacked Classifier']\n",
        "p1 = plt.bar(labels,\n",
        "        accuracy_scores,\n",
        "        color = colors)\n",
        "\n",
        "for i in p1:\n",
        "    height = i.get_height()\n",
        "    plt.annotate( \"{}%\".format(height),(i.get_x() + i.get_width()/2, height+.05),ha=\"center\",va=\"bottom\",fontsize=15)\n",
        "\n",
        "plt.xlabel('Classifiers',fontsize=18)\n",
        "plt.ylabel('Accuracy',fontsize=18)\n",
        "plt.title('Testing accuracy of algorithms',fontsize=20)\n",
        "plt.xticks(rotation=45,fontsize=12)\n",
        "plt.yticks(fontsize=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQ0QTjmZgmZt"
      },
      "source": [
        "# **Comparison table of training performance parameters**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ok1ZrKE_gmZu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cc6dd5a8-9864-4410-dc76-41128f1e080a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Accuracy         MCC          F1      Recall\n",
              "knn        86.500   73.625991   86.461954   92.620865\n",
              "svm_rbf   100.000  100.000000  100.000000  100.000000\n",
              "dt         83.375   67.047210   83.355894   87.531807\n",
              "rf         98.625   97.256162   98.624796   97.964377\n",
              "stack     100.000  100.000000  100.000000  100.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-14c476d0-f251-4bc9-bb97-311753d29d8f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>MCC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>86.500</td>\n",
              "      <td>73.625991</td>\n",
              "      <td>86.461954</td>\n",
              "      <td>92.620865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm_rbf</th>\n",
              "      <td>100.000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>83.375</td>\n",
              "      <td>67.047210</td>\n",
              "      <td>83.355894</td>\n",
              "      <td>87.531807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>98.625</td>\n",
              "      <td>97.256162</td>\n",
              "      <td>98.624796</td>\n",
              "      <td>97.964377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stack</th>\n",
              "      <td>100.000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14c476d0-f251-4bc9-bb97-311753d29d8f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-e896987e-941b-44d2-87ff-ebf86d710cc6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e896987e-941b-44d2-87ff-ebf86d710cc6')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e896987e-941b-44d2-87ff-ebf86d710cc6 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-14c476d0-f251-4bc9-bb97-311753d29d8f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14c476d0-f251-4bc9-bb97-311753d29d8f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 598
        }
      ],
      "source": [
        "acc_train_list = {'knn':knn_train_accuracy,\n",
        "'svm_rbf': svm_rbf_train_accuracy,\n",
        "'dt': dt_train_accuracy,\n",
        "'rf': rf_train_accuracy,\n",
        "'stack': stack_model_train_accuracy}\n",
        "\n",
        "mcc_train_list = {'knn':knn_train_mcc,\n",
        "'svm_rbf': svm_rbf_train_mcc,\n",
        "'dt': dt_train_mcc,\n",
        "'rf': rf_train_mcc,\n",
        "'stack': stack_model_train_mcc}\n",
        "\n",
        "f1_train_list = {'knn':knn_train_f1,\n",
        "'svm_rbf': svm_rbf_train_f1,\n",
        "'dt': dt_train_f1,\n",
        "'rf': rf_train_f1,\n",
        "'stack': stack_model_train_f1}\n",
        "\n",
        "recall_train_list = {'knn':knn_train_recall,\n",
        "'svm_rbf': svm_rbf_train_recall,\n",
        "'dt': dt_train_recall,\n",
        "'rf': rf_train_recall,\n",
        "'stack': stack_model_train_recall}\n",
        "\n",
        "acc_train_df = pd.DataFrame.from_dict(acc_train_list, orient='index', columns=['Accuracy'])\n",
        "mcc_train_df = pd.DataFrame.from_dict(mcc_train_list, orient='index', columns=['MCC'])\n",
        "f1_train_df = pd.DataFrame.from_dict(f1_train_list, orient='index', columns=['F1'])\n",
        "rc_train_df = pd.DataFrame.from_dict(recall_train_list, orient='index', columns=['Recall'])\n",
        "df = pd.concat([acc_train_df, mcc_train_df, f1_train_df, rc_train_df], axis=1)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DRyPfhblgmZu"
      },
      "source": [
        "# **Comparison table of testing performance parameters**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L2szrAhsgmZu",
        "outputId": "c2d93b2d-ad90-43d4-b988-f44b87961b63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Accuracy        MCC         F1     Recall\n",
              "knn          72.0  43.568568  71.777846  79.439252\n",
              "svm_rbf      84.0  72.214633  83.767719  70.093458\n",
              "dt           73.0  45.583703  72.835633  79.439252\n",
              "rf           82.5  64.785922  82.458059  85.981308\n",
              "stack        87.5  75.525561  87.510315  83.177570"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c61a68d8-d8e9-482c-bf1c-37089b4079f6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>MCC</th>\n",
              "      <th>F1</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>72.0</td>\n",
              "      <td>43.568568</td>\n",
              "      <td>71.777846</td>\n",
              "      <td>79.439252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm_rbf</th>\n",
              "      <td>84.0</td>\n",
              "      <td>72.214633</td>\n",
              "      <td>83.767719</td>\n",
              "      <td>70.093458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>73.0</td>\n",
              "      <td>45.583703</td>\n",
              "      <td>72.835633</td>\n",
              "      <td>79.439252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>82.5</td>\n",
              "      <td>64.785922</td>\n",
              "      <td>82.458059</td>\n",
              "      <td>85.981308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stack</th>\n",
              "      <td>87.5</td>\n",
              "      <td>75.525561</td>\n",
              "      <td>87.510315</td>\n",
              "      <td>83.177570</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c61a68d8-d8e9-482c-bf1c-37089b4079f6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-73e17af2-0992-4dd8-853c-b27a00846b46\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-73e17af2-0992-4dd8-853c-b27a00846b46')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-73e17af2-0992-4dd8-853c-b27a00846b46 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c61a68d8-d8e9-482c-bf1c-37089b4079f6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c61a68d8-d8e9-482c-bf1c-37089b4079f6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 599
        }
      ],
      "source": [
        "acc_test_list = {'knn':knn_test_accuracy,\n",
        "'svm_rbf': svm_rbf_test_accuracy,\n",
        "'dt': dt_test_accuracy,\n",
        "'rf': rf_test_accuracy,\n",
        "'stack': stack_model_test_accuracy}\n",
        "\n",
        "mcc_test_list = {'knn':knn_test_mcc,\n",
        "'svm_rbf': svm_rbf_test_mcc,\n",
        "'dt': dt_test_mcc,\n",
        "'rf': rf_test_mcc,\n",
        "'stack': stack_model_test_mcc}\n",
        "\n",
        "f1_test_list = {'knn':knn_test_f1,\n",
        "'svm_rbf': svm_rbf_test_f1,\n",
        "'dt': dt_test_f1,\n",
        "'rf': rf_test_f1,\n",
        "'stack': stack_model_test_f1}\n",
        "\n",
        "recall_test_list = {'knn':knn_test_recall,\n",
        "'svm_rbf': svm_rbf_test_recall,\n",
        "'dt': dt_test_recall,\n",
        "'rf': rf_test_recall,\n",
        "'stack': stack_model_test_recall}\n",
        "\n",
        "acc_test_df = pd.DataFrame.from_dict(acc_test_list, orient='index', columns=['Accuracy'])\n",
        "mcc_test_df = pd.DataFrame.from_dict(mcc_test_list, orient='index', columns=['MCC'])\n",
        "f1_test_df = pd.DataFrame.from_dict(f1_test_list, orient='index', columns=['F1'])\n",
        "rc_test_df = pd.DataFrame.from_dict(recall_test_list, orient='index', columns=['Recall'])\n",
        "df = pd.concat([acc_test_df, mcc_test_df, f1_test_df, rc_test_df], axis=1)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JvETLX1kgmZu"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}